Optical imaging and/or measuring apparatus

ABSTRACT

The present disclosure provides techniques and apparatus for capturing an image of a person&#39;s retina fundus, identifying the person, accessing various electronic records (including health records) or accounts or devices associated with the person, determining the person&#39;s predisposition to certain diseases, and/or diagnosing health issues of the person. Some embodiments provide imaging apparatus having one or more imaging devices for capturing one or more images of a person&#39;s eye(s). Imaging apparatus described herein may include electronics for analyzing and/or exchanging captured image and/or health data with other devices. In accordance with various embodiments, imaging apparatus described herein may be alternatively or additionally configured for biometric identification and/or health status determination techniques, as described herein.

FIELD OF THE DISCLOSURE

The present application relates to biometric identification, such asusing a person's retina fundus.

BACKGROUND

Present techniques for identifying a person, accessing a person'sprivate devices or accounts, determining a health status of a person,and/or diagnosing a health condition of the person would benefit fromimprovement.

BRIEF SUMMARY

Some aspects of the present disclosure provide an imaging apparatus,comprising a housing configured to accommodate a first eye and a secondeye of a person, the housing having disposed therein, an opticalcoherence tomography (OCT) device configured for imaging and/ormeasuring a retina of the first eye, and a fluorescence deviceconfigured for imaging and/or measuring a retina of the second eye.

Some aspects of the present disclosure provide an imaging apparatus,comprising a binocular-shaped housing having disposed therein aplurality of imaging devices including an optical imaging device and afluorescence imaging device, wherein the optical imaging device and thefluorescence imaging device are configured to perform imaging and/ormeasuring using a same optical component.

Some aspects of the present disclosure provide an imaging apparatus,comprising a housing having a plurality of imaging devices and at leasttwo lenses disposed therein, wherein the at least two lenses arerespectively aligned with at least two of the plurality of imagingdevices for imaging and/or measuring at least one of first and secondeyes of a person.

Some aspects of the present disclosure provide a stand for an imagingapparatus, the stand comprising a holding portion configured to receivea housing of the imaging apparatus and a base coupled to the holdingportion and configured to support the imaging apparatus when received inthe holding portion.

The foregoing summary is not intended to be limiting. In addition,various embodiments may include any aspects of the disclosure eitheralone or in combination.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a block diagram of a cloud-connected system for biometricidentification and health or other account access, in accordance withsome embodiments of the technology described herein.

FIG. 2 is a block diagram an exemplary device for local biometricidentification and health or other account access, in accordance withsome embodiments of the system illustrated in FIG. 1.

FIG. 3 is a flow diagram illustrating an exemplary method for capturingone or more retina fundus images and extracting image data from thecaptured image(s), in accordance with the embodiments of FIGS. 1-2.

FIG. 4 is a side view of a person's retina fundus including variousfeatures which may be captured in one or more image(s) and/or indicatedin data extracted from the image(s), in accordance with the method ofFIG. 3.

FIG. 5A is a block diagram of an exemplary convolutional neural network(CNN), in accordance with some embodiments of the method of FIG. 3.

FIG. 5B is a block diagram of an exemplary convolutional neural network(CNN), in accordance with some embodiments of the CNN of FIG. 5A.

FIG. 5C is a block diagram of an exemplary recurrent neural network(RNN) including a long short-term memory (LSTM) network, in accordancewith alternative embodiments of the CNN of FIG. 5A.

FIG. 6 is a block diagram of an exemplary fully convolutional neuralnetwork (FCNN), in accordance with some embodiments of the method ofFIG. 3.

FIG. 7 is a block diagram of an exemplary convolutional neural network(CNN), in accordance with alternative embodiments of the method of FIG.3.

FIG. 8 is a block diagram of an exemplary convolutional neural network(CNN), in accordance with further alternative embodiments of the methodof FIG. 3.

FIG. 9 is a flow diagram illustrating an exemplary method foridentifying a person, in accordance with the embodiments of FIGS. 1-2.

FIG. 10A is a flow diagram of a method for template-matching retinafundus features, in accordance with some embodiments of the method ofFIG. 9.

FIG. 10B is a flow diagram of a method for comparing translationally androtationally invariant features of a person's retina fundus, inaccordance with some embodiments of the method of FIG. 9.

FIG. 11 is a block diagram illustrating an exemplary user interface inaccordance with the embodiments of FIGS. 1-2.

FIG. 12 is a block diagram illustrating an exemplary distributed ledger,components of which are accessible over a network, in accordance withsome embodiments of the technology described herein.

FIG. 13A is a flow diagram illustrating an exemplary method includingtransmitting, over a communication network, first image data associatedwith and/or including a first image of a person's retina fundus, andreceiving, over the communication network, an identity of the person, inaccordance with some embodiments of the technology described herein.

FIG. 13B is a flow diagram illustrating an exemplary method including,based on first image data associated with and/or including a first imageof a person's retina fundus, identifying the person, and, based on afirst biometric characteristic of the person, verifying an identity ofthe person, in accordance with some embodiments of the technologydescribed herein.

FIG. 13C is a flow diagram illustrating an exemplary method including,based on first image data associated with and/or including a first imageof a person's retina fundus, identifying the person and updating storeddata associated with a plurality of retina fundus images, in accordancewith some embodiments of the technology described herein.

FIG. 13D is a flow diagram illustrating an exemplary method includingproviding, as a first input to a trained statistical classifier (TSC),first image data associated with and/or including a first image of aperson's retina fundus, and, based on at least one output from the TSC,identifying the person, in accordance with some embodiments of thetechnology described herein.

FIG. 13E is a flow diagram illustrating an exemplary method including,based on first image data associated with and/or including a first imageof a person's retina fundus, identifying the person, and determining amedical condition of the person, in accordance with some embodiments ofthe technology described herein.

FIG. 13F is a flow diagram illustrating an exemplary method includingproviding, as a first input to a trained statistical classifier (TSC),first image data associated with and/or including a first image of aperson's retina fundus, based on at least one output from the TSC,identifying the person at step, and determining a medical condition ofthe person, in accordance with some embodiments of the technologydescribed herein.

FIG. 14A is a front perspective view of an exemplary imaging apparatus,in accordance with some embodiments of the technology described herein.

FIG. 14B is a rear perspective, and partly transparent view of theimaging apparatus of FIG. 14A, in accordance with some embodiments ofthe technology described herein.

FIG. 15 is a bottom view of an alternative exemplary imaging apparatus,in accordance with some embodiments of the technology described herein.

FIG. 16A is a rear perspective view of a further exemplary imagingapparatus, in accordance with some embodiments of the technologydescribed herein.

FIG. 16B is an exploded view of the imaging apparatus of FIG. 16A, inaccordance with some embodiments of the technology described herein.

FIG. 16C is a side view of a person using the imaging apparatus of FIG.16A to image one or each of the person's eyes, in accordance with someembodiments of the technology described herein.

FIG. 16D is a perspective view of the imaging apparatus of FIG. 16Asupported by a stand, in accordance with some embodiments of thetechnology described herein.

DETAILED DESCRIPTION

The inventors have discovered that a captured image of a person's retinafundus can be used to identify a person, determine the person'spredisposition to certain diseases, and/or diagnose health issues of theperson. Accordingly, the inventors have developed techniques forcapturing an image of a person's retina fundus. Further, the inventorshave developed techniques for identifying a person, accessing variouselectronic records (including health records) or accounts or devicesassociated with the person, determining the person's predisposition tocertain diseases, and/or diagnosing health issues of the person.

Some embodiments of the technology described herein provide systems forcloud-based biometric identification capable of protecting sensitivedata such as electronic records or accounts stored on the cloud. Someembodiments provide systems for storing health information associatedwith various patients on the cloud, and/or for protecting patients'health information with a biometric identification system such that thehealth information may be more accessible to patients withoutsacrificing security or confidentiality. In some embodiments, abiometric identification system may be integrated together with a systemfor storing health information and/or for determining a medicalcondition of the patients, such that data from one or more capturedimage(s) used to identify a person may also be used to update theperson's health information, and/or to determine a medical condition ofthe person.

The inventors have recognized several problems in current securitysystems such as for authentication using alphanumeric password orpasscode systems and various forms of biometric security. Alphanumericpassword or passcode systems may be susceptible to hacking, for exampleby brute force (e.g., attempting every possible alphanumericcombination). In such cases, users may strengthen their passwords byusing a long sequence of characters or by using a greater diversity ofcharacters (such as punctuation or a mix of letters and numbers).However, in such methods, passwords are more difficult for users toremember. In other cases, users may select passwords or passcodes whichincorporate personal information (e.g., birth dates, anniversary dates,or pet names), which may be easier to remember but also may be easierfor a third party to guess.

While some biometric security systems are configured for authenticationsuch as by voiceprint, face, fingerprint, and iris identification mayprovide improved fraud protection compared to password and passcodesystems, the inventors have recognized that these systems end up beinginefficient at identifying the correct person. Typically, these systemswill either have a high false acceptance rate or a false rejection rate.A high false acceptance rate makes fraudulent activity easier, and ahigh false rejection rate makes it more difficult to positively identifythe patient. In addition, while other systems such as DNA identificationare effective at identifying the correct person, the inventors haverecognized that such systems are overly invasive. For example, DNAidentification requires an invasive testing procedure such as a blood orsaliva sample, which becomes increasingly impractical and expensive asidentification is done with increasing frequency. Further, DNAidentification is expensive and may be susceptible to fraud by stealingan artifact such as a hair containing DNA.

To solve the problems associated with existing systems, the inventorshave developed biometric identification systems configured to identify aperson using a captured image of the person's retina fundus. Suchsystems provide a minimally invasive imaging method with a low falseacceptance rate and a low false rejection rate.

Moreover, biometric identification as described herein is furtherdistinguished from authentication techniques of conventional systems inthat biometric identification systems described herein may be configuredto not only confirm the person's identity but actually to determine theperson's identity without needing any information from the person.Authentication typically requires that the person provide identificationinformation along with a password, passcode, or biometric measure todetermine whether the identification information given matches thepassword, passcode, or biometric measure. In contrast, systems describedherein may be configured to determine the identity of a person based onone or more captured images of the person's retina fundus. In someembodiments, further security methods such as a password, passcode, orbiometric measure such as voiceprint, face, fingerprint, and iris of theperson may be obtained for further authentication to supplement thebiometric identification. In some embodiments, a person may provideidentification information to a biometric identification system inaddition to the captured image(s) of the person's retina fundus.

The inventors have further recognized that retina fundus features whichmay be used to identify a person from a captured image may also be usedas indicators of the person's predisposition to certain diseases, andeven to diagnose a medical condition of the person. Accordingly, systemsdescribed herein may be alternatively or additionally configured todetermine the person's predisposition to various diseases, and todiagnose some health issues of the person. For example, upon capturingor otherwise obtaining one or more images of the person's retina fundusfor identification, the system may also make such determinations ordiagnoses based on the image(s).

Turning to the figures, FIGS. 1-2 illustrate exemplary systems anddevices configured to implement techniques for any or each of biometricidentification, health information management, medical conditiondetermination, and/or electronic account access. The description ofthese techniques which follows the description of the systems anddevices will refer back to the systems and devices illustrated in FIGS.1-2.

Referring to FIGS. 1-2, FIG. 1 illustrates a cloud-connected system inwhich a device may communicate with a remote computer to perform variousoperations associated with the techniques described herein. In contrastto FIG. 1, FIG. 2 illustrates a device which may be configured toperform any or all of the techniques described herein locally on thedevice.

With reference to FIG. 1, the inventors have recognized that theprocessing required for biometric identification, health informationmanagement, and other tasks on a user-end device may require, at leastin some circumstances, power-hungry and/or expensive processing and/ormemory components. To solve these problems, the inventors have developedcloud-connected systems and devices which may offset some or all of themost demanding processing and/or memory intensive tasks onto a remotecomputer, such that user-end devices may be implemented having lessexpensive and more power efficient hardware. In some instances, thedevice may only need to capture an image of a person and transmit dataassociated with the image to the remote computer. In such instances, thecomputer may perform biometric identification, access/update healthinformation and/or account information, and/or determine a medicalcondition based on the image data, and transmit the resulting data backto the device. Because the device may only capture an image and transmitdata associated with the image to the computer, the device may requirevery little processing power and/or memory, which facilitates acorresponding decrease in both cost and power consumption at the deviceend. Thus, the device may have an increased battery life and may be moreaffordable to the end user.

FIG. 1 is a block diagram of exemplary system 100 including device 120 aand computer 140, which are connected to communication network 160.

Device 120 a includes imaging apparatus 122 a and processor 124 a. Insome embodiments, device 120 a may be a portable device such as a mobilephone, a tablet computer, and/or a wearable device such as a smartwatch. In some embodiments, device 120 a may include a standalonenetwork controller for communicating over communication network 160.Alternatively, the network controller may be integrated with processor124 a. In some embodiments, device 120 a may include one or moredisplays for providing information via a user interface. In someembodiments, imaging apparatus 122 a may be packaged separately fromother components of device 120 a. For example, imaging apparatus 122 amay be communicatively coupled to the other components, such as via anelectrical cable (e.g., universal serial bus (USB) cable) and/or a wiredor wireless network connection. In other embodiments, imaging apparatus122 a may be packaged together with other components of device 120 a,such as within a same mobile phone or tablet computer housing, asexamples.

Computer 140 includes storage medium 142 and processor 144. Storagemedium 142 may contain images and/or data associated with images foridentifying a person. For example, in some embodiments, storage medium142 may contain retina fundus images and/or data associated with retinafundus images for comparing to retina fundus images of the person to beidentified.

In accordance with various embodiments, communication network 160 may bea local area network (LAN), a cell phone network, a Bluetooth network,the internet, or any other such network. For example, computer 140 maybe positioned in a remote location relative to device 120 a, such as aseparate room from device 120 a, and communication network 160 may be aLAN. In some embodiments, computer 140 may be located in a differentgeographical region from device 120 a, and may communicate over theinternet.

It should be appreciated that, in accordance with various embodiments,multiple devices may be included in place of or in addition to device120 a. For example, an intermediary device may be included in system 100for communicating between device 120 a and computer 140. Alternativelyor additionally, multiple computers may be included in place of or inaddition to computer 140 to perform various tasks herein attributed tocomputer 140.

FIG. 2 is a block diagram of exemplary device 120 b, in accordance withsome embodiments of the technology described herein. Similar to device120 a, device 120 b includes imaging apparatus 122 b and processor 124b, which may be configured in the manner described for device 120 a.Device 120 b may include one or more displays for providing informationvia a user interface. Device 120 b also includes storage medium 126.Data stored on storage medium 126, such as image data, healthinformation, account information, or other such data may facilitatelocal identification, health information management, medical conditiondetermination, and/or account access on device 120 b. It should beappreciated that device 120 b may be configured to perform any or alloperations associated with the techniques described herein locally, andin some embodiments may transmit data to a remote computer such ascomputer 140 so as to perform such operations remotely. For example,device 120 b may be configured to connect to communication network 160.

I. Techniques and Apparatus for Obtaining an Image of and/or Measuring aPerson's Retina

The inventors have developed techniques for capturing one or more imagesof a person's retina fundus and/or obtaining data associated with theimages, aspects of which are described with reference to FIGS. 1-2.

Imaging apparatus 122 a or 122 b may be configured to capture a singleimage of the person's retina fundus. Alternatively, imaging apparatus122 a or 122 b may be configured to capture multiple images of theperson's retina fundus. In some embodiments, imaging apparatus 122 a or122 b may be a 2-Dimensional (2D) imaging apparatus such as a digitalcamera. In some embodiments, imaging apparatus 122 a or 122 b may bemore advanced, such as incorporating Optical Coherence Tomography (OCT)and/or Fluorescence Lifetime Imaging Microscopy (FLIM). For example, insome embodiments, imaging apparatus 122 a or 122 b may be a retinalsensing device may be configured for widefield or scanning retina fundusimaging such as using white light or infrared (IR) light, fluorescenceintensity, OCT, or fluorescence lifetime data. Alternatively oradditionally, imaging apparatus 122 a or 122 b may be configured forone-dimensional (1D), 2-dimensional (2D), 3-dimensional (3D) or otherdimensional contrast imaging. Herein, fluorescence and lifetime areconsidered different dimensions of contrast. Images described herein maybe captured using any or each of a red information channel (e.g., havinga wavelength between 633-635 nm), a green information channel (e.g.,having a wavelength of approximately 532 nm), or any other suitablelight imaging channel(s). As a non-limiting example, a fluorescenceexcitation wavelength may be between 480-510 nm with an emissionwavelength from 480-800 nm.

Imaging apparatus 122 a or 122 b may be packaged separately from othercomponents of device 120 a or 120 b, such that it may be positioned neara person's eye(s). In some embodiments, device 120 a or device 120 b maybe configured to accommodate (e.g., conform to, etc.) a person's face,such as specifically around the person's eye(s). Alternatively, device120 a or 120 b may be configured to be held in front of the person'seye(s). In some embodiments, a lens of imaging apparatus 122 a or 122 bmay be placed in front of the user's eye during imaging of the person'sretina fundus. In some embodiments, imaging apparatus 122 a or 122 b maybe configured to capture one or more images in response to a userpressing a button on device 120 a or 120 b. In some embodiments, imagingapparatus 122 a or 122 b may be configured to capture the image(s)responsive to a voice command from the user. In some embodiments,imaging apparatus 122 a may be configured to capture the image(s)responsive to a command from computer 140. In some embodiments, imagingapparatus 122 a or 122 b may be configured to capture the image(s)automatically upon device 120 a or 120 b sensing the presence of theperson, such as by detecting the person's retina fundus in view ofimaging apparatus 122 a or 122 b.

The inventors have also developed novel and improved imaging apparatushaving enhanced imaging functionality and a versatile form factor. Insome embodiments, imaging apparatus described herein may include two ormore imaging devices, such as OCT and/or FLIM devices within a commonhousing. For example, a single imaging apparatus may include a housingshaped to support OCT and FLIM devices within the housing along withassociated electronics for performing imaging and/or accessing the cloudfor image storage and/or transmission. In some embodiments, electronicsonboard the imaging apparatus may be configured to perform variousprocessing tasks described herein, such as identifying a user of theimaging apparatus (e.g., by imaging the person's retina fundus),accessing a user's electronic health records, and/or determine a healthstatus or medical condition of the user.

In some embodiments, imaging apparatus described herein may have a formfactor that is conducive to imaging both of a person's eyes (e.g.,simultaneously). In some embodiments, imaging apparatus described hereinmay be configured for imaging each eye with a different imaging deviceof the imaging apparatus. For example, as described further below, theimaging apparatus may include a pair of lenses held in a housing of theimaging apparatus for aligning with a person's eyes, and the pair oflenses may also be aligned with respective imaging devices of theimaging apparatus. In some embodiments, the imaging apparatus mayinclude a substantially binocular shaped form factor with an imagingdevice positioned on each side of the imaging apparatus. Duringoperation of the imaging apparatus, a person may simply flip thevertical orientation of the imaging apparatus (e.g., by rotating thedevice about an axis parallel to the direction in which imaging isperformed). Accordingly, the imaging apparatus may transition fromimaging the person's right eye with a first imaging device to imagingthe right eye with a second imaging device, and likewise, transitionfrom imaging the person's left eye with the second imaging device toimaging the left eye with the first imaging device. In some embodiments,imaging apparatus described herein may be configured for mounting on atable or desk, such as on a stand. For example, the stand may permitrotation of the imaging apparatus about one or more axes to facilitaterotation by a user during operation.

It should be appreciated that aspects of the imaging apparatus describedherein may be implemented using a different form factor thansubstantially binocular shaped. For instance, embodiments having a formfactor different than substantially binocular shaped may be otherwiseconfigured in the manner described herein in connection with theexemplary imaging apparatus described below. For example, such imagingapparatus may be configured to image one or both of a person's eyessimultaneously using one or more imaging devices of the imagingapparatus.

One example of an imaging apparatus according to the technologydescribed herein is illustrated in FIGS. 14A-14B. As shown in FIG. 14A,imaging apparatus 1400 includes a housing 1401 with a first housingsection 1402 and a second housing section 1403. In some embodiments, thefirst housing section 1402 may accommodate a first imaging device 1422of the imaging apparatus 1400, and the second housing section 1403 mayaccommodate a second imaging device 1423 of the imaging apparatus. Asillustrated in FIGS. 14A-14B, housing 1401 is substantially binocularshaped.

In some embodiments, the first and second imaging devices 1422 mayinclude an optical imaging device, a fluorescent imaging device, and/oran OCT imaging device. For example, in one embodiment, the first imagingdevice 1422 may be an OCT imaging device, and the second imaging device1423 may be an optical and fluorescent imaging device. In someembodiments, the imaging apparatus 1400 may include only a singleimaging device 1422 or 1423, such as only an optical imaging device oronly a fluorescent imaging device. In some embodiments, first and secondimaging devices 1422 and 1423 may share one or more optical componentssuch as lenses (e.g., convergent, divergent, etc.), mirrors, and/orother imaging components. For instance, in some embodiments, first andsecond imaging devices 1422 and 1423 may share a common optical path. Itis envisioned that the devices may operate independently or in common.Each may be an OCT imaging device, each may be a fluorescent imagingdevice, or both may be one or the other. Both eyes may be imaged and/ormeasured simultaneously, or each eye may be imaged and/or measuredseparately.

Housing sections 1402 and 1403 may be connected to a front end of thehousing 1401 by a front housing section 1405. In the illustrativeembodiment, the front housing section 1405 is shaped to accommodate thefacial profile of a person, such as having a shape that conforms to ahuman face. When accommodating a person's face, the front housingsection 1405 may further provide sight-lines from the person's eyes tothe imaging devices 1422 and/or 1423 of the imaging apparatus 1400. Forexample, the front housing section 1405 may include a first opening 1410and a second opening 1411 that correspond with respective openings inthe first housing section 1402 and the second housing section 1403 toprovide minimally obstructed optical paths between the first and secondoptical devices 1422 and 1423 and the person's eyes. In someembodiments, the openings 1410 and 1410 may be covered with one or moretransparent windows (e.g., each having its own window, having a sharedwindow, etc.), which may include glass or plastic.

First and second housing sections 1402 and 1403 may be connected at arear end of the housing 1401 by a rear housing section 1404. The rearhousing section 1404 may be shaped to cover the end of the first andsecond housing sections 1402 and 1403 such that light in an environmentof the imaging apparatus 1400 does not enter the housing 1401 andinterfere with the imaging devices 1422 or 1423.

In some embodiments, imaging apparatus 1400 may be configured forcommunicatively coupling to another device, such as a mobile phone,desktop, laptop, or tablet computer, and/or smart watch. For example,imaging apparatus 1400 may be configured for establishing a wired and/orwireless connection to such devices, such as by USB and/or a suitablewireless network. In some embodiments, housing 1401 may include one ormore openings to accommodate one or more electrical (e.g., USB) cables.In some embodiments, housing 1401 may have one or more antennas disposedthereon for transmitting and/or receiving wireless signals to or fromsuch devices. In some embodiments, imaging devices 1422 and/or 1423 maybe configured for interfacing with the electrical cables and/orantennas. In some embodiments, imaging devices 1422 and/or 1423 mayreceive power from the cables and/or antennas, such as for charging arechargeable battery disposed within the housing 1401.

During operation of the imaging apparatus 1400, a person using theimaging apparatus 1400 may place the front housing section 1405 againstthe person's face such that the person's eyes are aligned with openings1410 and 1411. In some embodiments, the imaging apparatus 1400 mayinclude a gripping member (not shown) coupled to the housing 1401 andconfigured for gripping by a person's hand. In some embodiments, thegripping member may be formed using a soft plastic material, and may beergonomically shaped to accommodate the person's fingers. For instance,the person may grasp the gripping member with both hands and place thefront housing section 1405 against the person's face such that theperson's eyes are in alignment with openings 1410 and 1411.Alternatively or additionally, the imaging apparatus 1400 may include amounting member (not shown) coupled to the housing 1401 and configuredfor mounting the imaging apparatus 1400 to a mounting arm, such as formounting the imaging apparatus 1400 to a table or other equipment. Forinstance, when mounted using the mounting member, the imaging apparatus1400 may be stabilized in one position for use by a person without theperson needing to hold the imaging apparatus 1400 in place.

In some embodiments, the imaging apparatus 1400 may employ a fixator,such as a visible light projection from the imaging apparatus 1400towards the person's eyes, such as along a direction in which theopenings 1410 and 1411 are aligned with the person's eyes, for example.In accordance with various embodiments, the fixator may be a brightspot, such as a circular or elliptical spot, or an image, such as animage or a house or some other object. The inventors recognized that aperson will typically move both eyes in a same direction to focus on anobject even when only one eye perceives the object. Accordingly, in someembodiments, the image apparatus 1400 may be configured to provide thefixator to only one eye, such as using only one opening 1410 or 1411. Inother embodiments, fixators may be provided to both eyes, such as usingboth openings 1410 and 1411.

FIG. 15 illustrates a further embodiment of an imaging apparatus 1500,in accordance with some embodiments. As shown, imaging apparatus 1500includes housing 1501, within which one or more imaging devices (notshown) may be disposed. Housing 1501 includes first housing section 1502and second housing section 1503 connected to a central housing portion1504. The central housing portion 1504 may include and/or operate as ahinge connecting the first and second housing sections 1502 and 1503,and about which the first and second housing portions 1502 and 1503 mayrotate. By rotating the first and/or second housing sections 1502 and/or1503 about the central housing portion 1504, a distance separating thefirst and second housing sections 1502 and 1503 may be increased ordecreased accordingly. Before and/or during operation of the imagingapparatus 1500, a person may rotate the first and second housingsections 1502 and 1503 to accommodate a distance separating the person'seyes, such as to facilitate alignment of the person's eyes with openingsof the first and second housing sections 1502 and 1503.

The first and second housing sections 1502 and 1503 may be configured inthe manner described for first and second housing sections 1402 and 1403in connection with FIGS. 14A-14B. For instance, each housing section mayaccommodate one or more imaging devices therein, such as an opticalimaging device, a fluorescent imaging device, and/or an OCT imagingdevice. In FIG. 15, each housing section 1502 and 1503 is coupled to aseparate one of front housing sections 1505A and 1505B. Front housingsections 1505A and 1505B may be shaped to conform to the facial profileof a person using the imaging apparatus 1500, such as conforming toportions of the person's face proximate the person's eyes. In oneexample, the front housing sections 1505A and 1505B may be formed usinga pliable plastic that may conform to the person's facial profile whenplaced against the person's face. Front housing sections 1505A and 1505Bmay have respective openings 1511 and 1510 that correspond with openingsof first and second housing sections 1502 and 1503, such as in alignmentwith the openings of the first and second housing sections 1502 and 1503to provide minimally obstructed optical paths from the person's eyes tothe imaging devices of the imaging apparatus 1500. In some embodiments,the openings 1510 and 1511 may be covered with a transparent window madeusing glass or plastic.

In some embodiments, the central housing section 1504 may include one ormore electronic circuits (e.g., integrated circuits, printed circuitboards, etc.) for operating the imaging apparatus 1500. In someembodiments, one or more processors of device 120 a and/or 120 b may bedisposed in central housing section 1504, such as for analyzing datacaptured using the imaging devices. The central housing section 1504 mayinclude wired and/or wireless means of electrically communicating toother devices and/or computers, such as described for imaging apparatus1400. For instance, further processing (e.g., as described herein) maybe performed by the devices and/or computers communicatively coupled toimaging apparatus 1500. In some embodiments, the electronic circuitsonboard the imaging apparatus 1500 may process captured image data basedon instructions received from such communicatively coupled devices orcomputers. In some embodiments, the imaging apparatus 1500 may initiatean image capture sequence based on instructions received from a devicesand/or computers communicatively coupled to the imaging apparatus 1500.In some embodiments, processing functionality described herein fordevice 120 a and/or 120 b may be performed using one or more processorsonboard the imaging apparatus.

As described herein including in connection with imaging apparatus 1400,imaging apparatus 1500 may include a gripping member and/or a mountingmember, and/or a fixator.

FIGS. 16A-16D illustrate a further embodiment of an imaging apparatus1600, according to some embodiments. As shown in FIG. 16A, imagingapparatus 1600 has a housing 1601, including multiple housing portions1601 a, 1601 b, and 1601 c. Housing portion 1601 a has a control panel1625 including multiple buttons for turning imaging apparatus 1600 on oroff, and for initiating scan sequences. FIG. 16B is an exploded view ofimaging apparatus 1600 illustrating components disposed within housing1601, such as imaging devices 1622 and 1623 and electronics 1620.Imaging devices 1622 and 1623 may include an optical imaging device, afluorescent imaging device, and/or an OCT imaging device, in accordancewith various embodiments, as described herein in connection with FIGS.14A-14B and 15. Imaging apparatus further includes front housing portion1605 configured to receive a person's eyes for imaging, as illustrated,for example, in FIG. 16C. FIG. 16D illustrates imaging apparatus 1600seated in stand 1650, as described further herein.

As shown in FIGS. 16A-16D, housing portions 1601 a and 1601 b maysubstantially enclose imaging apparatus 1600, such as by having all ormost of the components of imaging apparatus 1600 disposed betweenhousing portions 1601 a and 1601 b. Housing portion 1601 c may bemechanically coupled to housing portions 1601 a and 1601 b, such asusing one or more screws fastening the housing 1601 together. Asillustrated in FIG. 16B, housing portion 1601 c may have multiplehousing portions therein, such as housing portions 1602 and 1603 foraccommodating imaging devices 1622 and 1623. For example, in someembodiments, the housing portions 1602 and 1603 may be configured tohold imaging devices 1622 and 1623 in place. Housing portion 1601 c isfurther includes a pair of lens portions in which lenses 1610 and 1611are disposed. Housing portions 1602 and 1603 and the lens portions maybe configured to hold imaging devices 1622 and 1623 in alignment withlenses 1610 and 1611. Housing portions 1602 and 1603 may accommodatefocusing parts 1626 and 1627 for adjusting the foci of lenses 1610 and1611. Some embodiments may further include securing tabs 1628. Byadjusting (e.g., pressing, pulling, pushing, etc.) securing tabs 1628,housing portions 1601 a, 1601 b, and/or 1601 c may be decoupled from oneanother, such as for access to components of imaging apparatus 1600 formaintenance and/or repair purposes.

Electronics 1620 may be configured in the manner described forelectronics 1620 in connection with FIG. 15. Control panel 1625 may beelectrically coupled to electronics 1520. For example, the scan buttonsof control panel 1625 may be configured to communicate a scan command toelectronics 1620 to initiate a scan using imaging device 1622 and/or1623. As another example, the power button of control panel 1625 may beconfigured to communicate a power on or power off command to electronics1620. As illustrated in FIG. 16B, imaging apparatus 1600 may furtherinclude electromagnetic shielding 1624 configured to isolate electronics1620 from sources of electromagnetic interference (EMI) in thesurrounding environment of imaging apparatus 1600. Includingelectromagnetic shielding 1624 may improve operation (e.g., noiseperformance) of electronics 1620. In some embodiments, electromagneticshielding 1624 may be coupled to one or more processors of electronics1620 to dissipate heat generated in the one or more processors.

In some embodiments, imaging apparatus described herein may beconfigured for mounting to a stand, as illustrated in the example ofFIG. 16D. In FIG. 16D, imaging apparatus 1600 is supported by stand1650, which includes base 1652 and holding portion 1658. Base 1652 isillustrated including a substantially U-shaped support portion and hasmultiple feet 1654 attached to an underside of the support portion. Base1652 may be configured to support imaging apparatus 1600 above a tableor desk, such as illustrated in the figure. Holding portion 1658 may beshaped to accommodate housing 1601 of imaging apparatus 1600. Forexample, an exterior facing side of holding portion 1658 may be shapedto conform to housing 1601.

As illustrated in FIG. 16D, base 1652 may be coupled to holding portion1658 by a hinge 1656. Hinge 1656 may permit rotation about an axisparallel to a surface supporting base 1652. For instance, duringoperation of imaging apparatus 1600 and stand 1650, a person may rotateholding portion 1658, having imaging apparatus 1600 seated therein, toan angle comfortable for the person to image one or both eyes. Forexample, the person may be seated at a table or desk supporting stand1650. In some embodiments, a person may rotate imaging apparatus 1600about an axis parallel to an optical axis along which imaging deviceswithin imaging apparatus image the person's eye(s). For instance, insome embodiments, stand 1650 may alternatively or additionally include ahinge parallel to the optical axis.

In some embodiments, holding portion 1658 (or some other portion ofstand 1650) may include charging hardware configured to transmit powerto imaging apparatus 1600 through a wired or wireless connection. In oneexample, the charging hardware in stand 1650 may include a power supplycoupled to one or a plurality of wireless charging coils, and imagingapparatus 1600 may include wireless charging coils configured to receivepower from the coils in stand 1650. In another example, charginghardware in stand 1650 may be coupled to an electrical connector on anexterior facing side of holding portion 1658 such that a complementaryconnector of imaging apparatus 1600 interfaces with the connector ofstand 1650 when imaging apparatus 1600 is seated in holding portion1658. In accordance with various embodiments, the wireless charginghardware may include one or more power converters (e.g., AC to DC, DC toDC, etc.) configured to provide an appropriate voltage and current toimaging apparatus 1600 for charging. In some embodiments, stand 1650 mayhouse at least one rechargeable battery configured to provide the wiredor wireless power to imaging apparatus 1600. In some embodiments. Stand1650 may include one or more power connectors configured to receivepower from a standard wall outlet, such as a single-phase wall outlet.

In some embodiments, front housing portion 1605 may include multipleportions 1605 a and 1605 b. Portion 1605 a may be formed using amechanically resilient material whereas front portion 1605 b may beformed using a mechanically compliant material, such that front housingportion 1605 is comfortable for a user to wear. For example, in someembodiments, portion 1605 a may be formed using plastic and portion 1605b may be formed using rubber or silicone. In other embodiments, fronthousing portion 1605 may be formed using a single mechanically resilientor mechanically compliant material. In some embodiments, portion 1605 bmay be disposed on an exterior side of front housing portion 1605, andportion 1605 a may be disposed within portion 1605 b.

The inventors have recognized several advantages which may be gained bycapturing multiple images of the person's retina fundus. For instance,extracting data from multiple captured images facilitates biometricidentification techniques which are less costly to implement while alsobeing less susceptible to fraud. As described herein including withreference to section III, data extracted from captured images may beused to identify a person by comparing the captured image data againststored image data. In some embodiments, a positive identification may beindicated when the captured image data has at least a predetermineddegree of similarity to some portion of the stored image data. While ahigh predetermined degree of similarity (e.g., close to 100%) may bedesirable to prevent the system from falsely identifying a person, sucha high degree of required similarity conventionally results in a highfalse-rejection ratio (FRR), meaning that it is more difficult for thecorrect person to be positively identified. This may be because, whenidentifying a person using a single captured image of the person havinga low resolution and/or a low field-of-view, the captured image may notachieve the high predetermined degree of similarity, for example due tomissing or distorted features in the image. As a result, an imagingapparatus capable of capturing images with a high resolution and a highfield-of-view may be desirable to allow use of a high predetermineddegree of similarity without compromising FRR. However, a high qualityimaging apparatus capable of supporting a high predetermined degree ofsimilarity is typically more expensive than a simple digital camera. Theconventional alternative to using a more expensive imaging apparatus isto use a lower predetermined degree of similarity. However, such asystem may be more susceptible to fraud.

To solve this problem, the inventors have developed techniques forbiometric identification which may be performed using an ordinarydigital camera for enhanced flexibility. In contrast to single-imagecomparison systems, the inventors have developed systems which maycapture multiple images for comparison, which facilitates use of ahigher degree of similarity without requiring a higher resolution orfield-of-view imaging apparatus. In some embodiments, data may beextracted from multiple images of the person's retina fundus andcombined into a single set for comparison. For example, multiple imagesmay be captured by imaging apparatus 122 a or 122 b, each of which maybe slightly rotated from one another so as to capture different portionsof the person's retina fundus. In some embodiments, the person's eye(s)may rotate and/or may track imaging apparatus 122 a or 122 b.Accordingly, data indicative of features of the person's retina fundusmay be extracted from the images and combined into a dataset indicativeof locations of the various features. Because multiple images arecombined for use, no single captured image needs to be high resolutionor have a high field of view. Rather, a simple digital camera, such as adigital camera integrated with a mobile phone, may be used for imagingas described herein.

In some embodiments, system 100 or 120 b may be configured to verifyretina fundus identification using recorded biometric characteristics(e.g., multi-factor identification). For example, device 120 a or 120 bmay also include one or more biometric sensors such as a fingerprintreader and/or a microphone. Thus, device 120 a or 120 b may record oneor more biometric characteristics of a person, such as a fingerprintand/or a voiceprint of the person. Data indicative of features of thebiometric characteristic(s) may be extracted in the manner described forretina fundus images, and in the case of device 120 a, the data may betransmitted to computer 140 for verification. Accordingly, once anidentification is made based on the retina fundus image(s), thebiometric characteristic data may be compared against storedcharacteristic data associated with the person to verify the retinafundus identification for added security.

II. Techniques for Identifying a Person Based on a Retinal Image

The inventors have developed techniques for identifying a person basedon a retinal image of the person. The technique may include comparingdata extracted from one or more captured images of the person's retinafundus to stored data extracted from other retina fundus images.Techniques for extracting data from one or more captured images isdescribed herein including with reference to FIGS. 3-4. FIG. 3 providesan illustrative method for capturing one or more images of a person'sretina fundus and extracting data from the captured image(s), and FIG. 4illustrates some features of a person's retina fundus which may beindicated in data extracted from the image(s).

FIG. 3 is a flow diagram illustrating exemplary method 300 includingcapturing one or more retina fundus images at step 302 and extractingimage data from the image(s) at step 304. In accordance with theembodiment of FIG. 1, method 300 may be performed by device 120 a, oralternatively may be performed in part by device 120 a and in part bycomputer 140. In accordance with the embodiment of FIG. 2, method 300may be performed entirely by device 120 b.

Capturing the image(s) at step 302 may be performed in accordance withany or all embodiments of the technology described in section I.Extracting image data from the image(s) at step 304 may includeprocessor 124 a or 124 b obtaining the captured image(s) from imagingapparatus 122 a or 122 b and extracting data indicative of features ofthe person's retina fundus from the image(s). For example, the data mayinclude relative positions and orientations of the features. In someembodiments, feature data may be extracted from multiple captured imagesand combined into a single feature dataset. It should be appreciatedthat feature extraction at step 304 may be performed by computer 140.For example, in some embodiments of system 100, device 120 a may beconfigured to capture the image(s) and to transmit the image(s) tocomputer 140 for data extraction.

Also during step 304, the extracted data may be recorded on a storagemedium, such as storage medium 124 of device 120 b. In some embodimentsof cloud-based system 100, imaging apparatus 122 a may capture theimage(s) and/or extract data from the image(s) when device 120 a doesnot have access to communication network 160, and so processor 124 a maystore the image(s) and/or data on the storage medium at least until atime when it may be transmitted over communication network 160. In suchcases, processor 124 a may obtain the image(s) and/or data from thestorage medium shortly before transmitting the image(s) and/or data tocomputer 140. In some embodiments, the retina fundus image(s) may not becaptured by device 120 a or 120 b, but by a separate device. Theimage(s) may be transferred to device 120 a or 120 b, from which datamay be extracted and stored on the storage medium. Alternatively, thedata may also be extracted by the separate device and transferred todevice 120 a or to device 120 b. For example, device 120 a may be taskedwith passing the data to computer 140, or device 120 b may identify aperson or perform some other task based on the data.

FIG. 4 is a side view of retina fundus 400 including various featureswhich may be captured in one or more images at step 302 during method300 of FIG. 3, and/or may be indicated in data extracted from theimage(s) at step 304. For example, features of veins and arteries ofretina fundus 400 may be used to identify a person. Such features mayinclude branch endings 410 and bifurcations 420 of the veins andarteries. The inventors have recognized that, similar to infingerprinting, locations of branch endings 410 and bifurcations 420(sometimes referred to as “minutiae”) may be used as unique identifiers.Accordingly, in some embodiments, relative locations of branch endings410 and/or bifurcations 420 may be extracted from a single capturedimage and recorded in one or more datasets. In some instances, relativelocations of branch endings 410 and/or bifurcations 420 may be extractedfrom multiple captured images and combined into a single dataset. Forexample, an average relative location of each branch ending 410 and/orbifurcation 420 may be recorded in the dataset. In some embodiments,relative locations of specific veins or arteries such as nasal artery430, nasal vein 440, temporal artery 450, and/or temporal vein 460 maybe recorded in one or more datasets.

In some embodiments, data indicative of other features may be extractedinstead of or in addition to data for branch endings 410 and/orbifurcations 420 at step 304. For example, aspects of optic disc 470 oroptic disc edges such as a relative position within retina fundus 400may be recorded in a dataset. In some embodiments, data associated withoptic disc 470 may be recorded in a separate dataset from dataassociated with veins or arteries. Alternatively or additionally, dataindicative of a relative position of fovea 480 and/or macula 490 may berecorded in a dataset. Further features which may be indicated in dataextracted from the captured image(s) include the optic nerve, bloodvessel surroundings, AV nicks, drusen, retinal pigmentations, andothers.

In some embodiments, extracting any or all of the features describedabove may include solving segmentation of the image(s) into a fullspatial map including relative positions and orientations of theindividual features. For example, the spatial map may include a binarymask indicative of whether features such as branch endings 410 orbifurcations 420 are present at any particular location in the map. Insome embodiments, a relative angle indicating locations of the featuresmay be calculated based on the spatial map. To conserve storage spaceand/or simplify computing of the spatial map, thickness of some featuressuch as veins may be reduced to a single pixel width. Alternatively oradditionally, redundant data may be removed from the spatial map, suchas data resulting from a combination of multiple images.

In some embodiments, the feature data may include relative positions andorientations of translationally and rotationally invariant features tofacilitate a Scale Invariant Feature Transform (SIFT) and/or Speeded UpRobust Feature (SURF) comparison, as described herein including withreference to section III. For example, the extracted features describedabove may be Scale Invariant Feature Transform (SIFT) features and/orSpeeded Up Robust Features (SURF).

The inventors have also developed techniques for extracting data fromone or more captured images using a trained statistical classifier(TSC), in accordance with the embodiments illustrated in FIGS. 5A-5C, 6,and 7A-7B. For example, in some embodiments, step 304 of method 300 maybe performed by a TSC such as illustrated in the embodiments of FIGS.5A-5C, 6, and 7A-7B. One or more images(s) captured by imaging apparatus122 a or 122 b may be input to the TSC. The captured image(s) mayinclude data from one or more widefield or scanned retinal imagescollected from imag xing apparatus 122 a or 122 b such as by whitelight, IR, fluorescence intensity, OCT, or 1D, 2D or 3D fluorescencelifetime data. The TSC may be configured to identify and output aspectsof various retina fundus features in the image(s). The inventors haverecognized that implementing TSCs for extracting feature data fromcaptured images fzsacilitates identification using multiple capturedimages. For example, TSCs described herein may be configured to formpredictions based on individual images or groups of images. Thepredictions may be in the form of one or more outputs from the TSC. Eachoutput may correspond to a single image or to multiple images. Forexample, one output may indicate the likelihood of a particular retinafundus feature appearing in one or more locations in a given image.Alternatively, the output may indicate the likelihood of multiplefeatures appearing in one or more locations of the image. Further, theoutput may indicate the likelihood of a single feature or of multiplefeatures appearing in one or more locations in multiple images.

TSCs described herein may be implemented in software, in hardware, orusing any suitable combination of software and hardware. For example, aTSC may be executed on processor 124 a of device 120 a, processor 144 ofcomputer 140, and/or processor 124 b of device 120 b. In someembodiments, one or more machine learning software libraries may be usedto implement TSCs as described herein such as Theano, Torch, Caffe,Keras, and TensorFlow. These libraries may be used for training astatistical classifier such as a neural network, and/or for implementinga trained statistical classifier.

In some embodiments, data extraction using a TSC may take place ondevice 120 a, which may transmit the output of the TSC to computer 140over communication network 160. Alternatively, computer 140 may obtainthe captured image(s) from device 120 a and extract the captured imagedata from the captured image(s), for example using a TSC executed oncomputer 140. In accordance with the latter embodiment, device 120 a maybe configured to transmit the captured image(s) to computer 140 in theform of one or more compressed versions of the image(s), such asstandardized by the Joint Photographic Experts Group (JPEG), oralternatively as one or more uncompressed versions such as by PortableNetwork Graphic (PNG). In the embodiment of FIG. 2, device 120 b mayobtain the captured image data from the captured image by extraction,such as using a TSC executed on processor 124 b.

FIGS. 5A-5C, 6, and 7-8 illustrate aspects of neural network statisticalclassifiers for use in biometric security systems described herein. Inaccordance with the illustrative embodiments of FIGS. 5A-5B, a neuralnetwork statistical classifier may include a convolutional neuralnetwork (CNN). In accordance with the illustrative embodiments of FIGS.5A and 5C, the neural network statistical classifier may further includea recurrent neural network (RNN), such as a long short-term memory(LSTM) network. Alternatively, in accordance with the illustrativeembodiment of FIG. 6, the neural network statistical classifier mayinclude a fully convolutional neural network (FCNN). FIG. 7 illustratesan FCNN configured to identify boundaries of features in an image of aperson's retina fundus. FIG. 8 illustrates a CNN configured to identifyindividual voxels which has the advantage of higher invariance tolocations of various retina fundus features such as blood vessels.

FIGS. 5A and 5B are block diagrams of portions 500 a and 500 b formingan exemplary convolutional neural network (CNN) configured to extractdata from a captured image of a person's retina fundus. In theillustrative embodiment of FIGS. 5A and 5B, portion 500 a may beoperatively coupled to portion 500 b, such as with an output of portion500 a coupled to an input of portion 500 b.

As shown in FIG. 5A, portion 500 a of the CNN includes an alternatingseries of convolutional layers 510 a-510 g and pooling layers 520 a-520c. Image 530, which may be a 256 pixel by 256 pixel (256×256) image of aperson's retina fundus, is provided as an input to portion 500 a.Portion 500 a may be configured to obtain feature map 540 from image530, and to output feature map 540 to portion 500 b. Portion 500 b maybe configured to generate predictions 570 to indicate aspects of image530, such as locations of retina fundus features.

Prior to being input to portion 500 a, image 530 may be pre-processed,such as by resampling, filtering, interpolation, affine transformation,segmentation, erosion, dilation, metric calculations (i.e. minutia),histogram equalization, scaling, binning, cropping, color normalization,resizing, reshaping, background subtraction, edge enhancement, cornerdetection, and/or using any other suitable pre-processing techniques.Examples of pre-processing techniques include:

-   -   1. Rescale the images to have the same radius (e.g., 300        pixels),    -   2. Subtract the local average color, e.g., with the local        average mapped to 50% gray    -   3. Clip the images to a portion (e.g., 90%) of their size to        remove boundary effects. This may include cropping the images to        contain only retina pixels and testing the effect of histogram        equalization on the performance of the algorithm.    -   4. Crop the images to contain mostly retina pixels (note; if        using this, there may not be a need to rescale the image based        on radius.)

In some embodiments, image 530 may be a compressed or uncompressedversion of an image captured by imaging apparatus 122 a or 122 b.Alternatively, image 530 may be processed from one or more imagescaptured by imaging apparatus 122 a or 122 b. In some embodiments, image530 may include post-image reconstruction retina data such as one ormore 3D volumetric OCT images. Alternatively or additionally, image 530may include unprocessed portions of the captured image(s). For example,image 530 may include spectra from one or more spectral-domain OCTimages, fluorescence lifetime statistics, pre-filtered images, orpre-arranged scans. In some embodiments, image 530 may be associatedwith multiple 2D images corresponding to slices of the person's retinafundus. In some embodiments, the slices may be neighboring. For example,in accordance with various embodiments, image 530 may be associated withimages corresponding to two, three, four, or five respective neighboringslices. In some embodiments, image 530 may include one or more 2D imagesof one or more respective slices in which the blood vessels areprominent.

CNN 500 a is configured to process image 530 through convolutionallayers 510 a-510 g and pooling layers 520 a-520 c. In some embodiments,convolutional layers 510 a-510 g and pooling layers 520 a-520 c may betrained to detect aspects of retina fundus features in a captured image.First, CNN 500 a processes image 530 using convolutional layers 510 aand 510 b to obtain 32 256×256 feature maps 532. Next, after anapplication of pooling layer 520 a, which may be a max pooling layer,convolutional layers 510 c and 510 d are applied to obtain 64 128×128feature maps 534. Next, after an application of pooling layer 520 b,which may also be a max pooling layer, convolutional layers 510 e and510 f are applied to obtain 128 64×64 feature maps 536. Next, afterapplication of pooling layer 520 c and convolutional layer 510 g,resulting 256 32×32 feature maps 538 may be provided at output 540 as aninput for portion 500 b of the CNN illustrated in FIG. 5B. CNN portion500 a may be trained using gradient descent, stochastic gradientdescent, backpropagation, and/or other iterative optimizationtechniques.

In some embodiments, CNN 500 a may be configured to process a singleimage, such as a single slice of a person's retina fundus, at a time.Alternatively, in some embodiments, CNN 500 a may be configured toprocess multiple images, such as multiple neighboring slices from a 3Dvolumetric image, at the same time. The inventors have recognized thataspects such as branch endings, bifurcations, overlaps, sizings, orother such features may be computed using information from a singleslice or from multiple neighboring slices. In some embodiments,convolutions performed by convolutional layers 510 a-510 g on multipleslices of a person's retina fundus may be two-dimensional (2D) orthree-dimensional (3D). In some embodiments, CNN 500 a may be configuredto predict features for each slice only using information from thatparticular slice. Alternatively, in some embodiments, CNN 500 a may beconfigured to use information from that slice and also from one or moreneighboring slices. In some embodiments, CNN 500 a may include afully-3D processing pipeline such that features for multiple slices arecomputed concurrently using data present in all of the slices.

In FIG. 5B, portion 500 b lincludes convolutional layers 512 a-512 b andfully connected layers 560. Portion 500 b may be configured to receivefeature maps 538 from output 540 of portion 500 a. For example, portion500 b may be configured to process feature maps 538 throughconvolutional layers 512 a and 512 b to obtain 256 32×32 feature maps542. Then, feature maps 542 may be processed through fully connectedlayers 560 to generate predictions 570. For example, fully connectedlayers 560 may be configured to determine which retina fundus featuresare most likely to have been identified by convolutional layers 510a-510 g and 512 a-512 b and pooling layers 520 a-520 c using probabilitydistributions in feature maps 542. Accordingly, predictions 570 mayindicate aspects of retina fundus features within image 530. In someembodiments, predictions 570 may include probability values such as aprobabilistic heat-map corresponding to a calculated likelihood thatcertain features are located in certain areas of image 530. In someembodiments, predictions 570 may indicate relative locations and/orsizes of branch endings or bifurcations, or other such characteristics.

In accordance with the embodiment of FIGS. 5A-5C, portion 500 c may beoperatively coupled to portion 500 a illustrated in FIG. 5A. Forexample, portion 500 c may be coupled to output 540 in place of portion500 b. Portion 500 a illustrated in FIG. 5A is a CNN portion, andportion 500 c is a recurrent neural network (RNN) portion. Portion 500 cmay be used to model temporal constraints among input images provided asinputs over time. RNN portion 500 c may be implemented as a longshort-term memory (LSTM) neural network. Such a neural networkarchitecture may be used to process a series of images obtained byimaging apparatus 122 a or 122 b during performance of a monitoring task(a longitudinal series of images over time). For example, in accordancewith the embodiment of FIG. 1, device 120 a may transmit the series ofimages to computer 140. In some embodiments, device 120 a may transmittiming information of the series of images such as the time elapsedbetween each image in the series. The CNN-LSTM neural network of FIGS.5A and 5C may receive the series of images as inputs and combine retinafundus features derived from at least one earlier-obtained image withfeatures obtained from a later-obtained image to generate predictions580.

In some embodiments, the CNN and the CNN-LSTM illustrated in FIGS. 5A-5Cmay use a kernel size of 3 with a stride of 1 for convolutional layers,a kernel size of “2” for pooling layers, and a variance scalinginitializer. RNN portion 500 c may be trained using stochastic gradientdescent and/or backpropagation through time.

FIG. 6 is a block diagram of illustrative fully convolutional neuralnetwork (FCNN) 600. FCNN 600 includes output compressing portion 620 andinput expanding portion 660. Output compressive portion 620 includes aseries of alternating convolutional and pooling layers, which may beconfigured in the manner described for portion 500 a of FIG. 5A. Inputexpanding portion 660 includes a series of alternating convolutional anddeconvolutional layers, and center-of-mass layer 666. Center-of-masslayer 666 computes estimates as a center-of-mass computed from theregressed location estimates at each location.

In some embodiments, output compressing portion 620 and input expandingportion 660 are connected by processing path 640 a. Processing path 640a includes a long short-term memory (LSTM) portion, which may beconfigured in the manner described for RNN portion 500 c of FIG. 5C.Embodiments which include processing path 640 a may be used to modeltemporal constraints in the manner described for the CNN-LSTM of FIGS.5A and 5C. Alternatively, in accordance with other embodiments, outputcompressing portion 620 and input expanding portion 660 are connected byprocessing path 640 b. In contrast to processing path 640 a, processingpath 640 b includes a convolutional network (CNN) portion which may beconfigured in the manner described for CNN portion 500 b of FIG. 5B.

In some embodiments, FCNN 600 may use a kernel size of 3 forconvolutional layers with stride of 1, a kernel size of “2” for thepooling layers, a kernel of size 6 with stride 2 for deconvolutionallayers, and a variance scaling initializer.

The output of FCNN 600 may be a single-channel output having the samedimensionality as the input. Accordingly, a map of point locations suchas vessel characteristic points may be generated by introducing Gaussiankernel intensity profiles at the point locations, with FCNN 600 beingtrained to regress these profiles using mean-squared error loss.

FCNN 600 may be trained using gradient descent, stochastic gradientdescent, backpropagation, and/or other iterative optimizationtechniques.

In some embodiments, TSCs described herein may be trained using labeledimages. For example, the TSC may be trained using images of retinafundus features such as branch endings, bifurcations, or overlaps ofblood vessels, the optic disc, vessels, bifurcations, endings, overlaps,and fovea. The scans may be annotated manually by one or more clinicalexperts. In some embodiments, the annotations may include indications ofthe locations of the vessel overlap, bifurcation, and ending points. Insome embodiments, the annotations may include coverage of fullstructures like the full blood vessels, the optic disc, or the fovea.

The inventors have recognized that by configuring a TSC as a multi-taskmodel, the output of the TSC may be used to identify one or morelocations of features of a person's retina fundus, and also to segmentthe blood vessels. For example, blood vessels provide several featuresfor identifying a person, and so it is beneficial to use blood vessellabels to train a multi-task model, such that the model is configured toidentify the locations of the blood vessels more accurately.Accordingly, CNN portion 500 a and/or FCNN 600 may include a multi-taskmodel.

FIGS. 7 is a block diagram of fully convolutional neural network (FCNN)700, which may be configured to indicate locations of boundaries ofcertain retina fundus features such as blood vessels, optic disc, orfovea, in a captured image. Training FCNN 700 may involve zero-paddingtraining images, using convolutional kernels of size 3 and stride 1,using a max pooling kernel with of size 2, and deconvolution (upscaleand convolution) kernels with size 6 and size 2. The output of theneural network may indicate locations of boundaries of certain retinafundus features.

The inventors have recognized that some TSCs may be configured toclassify individual voxels, which has the advantage of higher invarianceto the location of various retina fundus features such as blood vessels.FIG. 8 is a block diagram of convolutional neural network (CNN) 800,which may be configured to indicate locations of boundaries of certainretina fundus features by classifying individual voxels. In someembodiments, CNN 800 may include convolutional kernels with size 5 andstride 1 at the first layer and kernels with size 3 in the subsequentlayers. In the illustrative embodiment of FIG. 8, CNN 800 is configuredfor an input neighborhood of 25. In other embodiments, CNN 800 may berepeated as a building block for different sizes of the inputneighborhood, such as 30 or 35. In some embodiments, largerneighborhoods may use a larger initial kernel size such as 7. Featuremaps of CNN 800 may be merged in the last feature layer and combined toyield a single prediction.

In an embodiment, saliency maps are created to understand which parts ofthe images contribute to the output by computing the gradient of anoutput category with respect to input image. This quantifies how theoutput category value changes with respect to small changes in the inputimage pixels. Visualizing these gradients as an intensity image provideslocalization of the attention.

The computation is basically the ratio of the gradient of outputcategory with respect to input image:

∂output/∂input

These gradients are used to highlight input regions that cause the mostchange in the output and thus highlight salient image regions that mostcontribute to the output.

It should be appreciated that the neural network architecturesillustrated in FIGS. 5A-5C, 6, and 7-8 are illustrative and thatvariations of these architectures are possible. For example, one or moreother neural network layers such as convolutional layers,deconvolutional layers, rectified linear unit layers, upsampling layers,concatenate layers, or pad layers may be introduced to any of the neuralnetwork architectures of FIGS. 5A-5C, 6, and 7-8 in addition to orinstead of one or more illustrated layers. As another example, thedimensionality of one or more layers may vary, and the kernel size forone or more convolutional, pooling, and/or deconvolutional layers mayalso vary. In addition, TSCs described herein may alternatively oradditionally include a support vector machine, a graphical model, aBayesian classifier, or a decision tree classifier.

The inventors have developed techniques for comparing data extractedfrom one or more captured images to stored data extracted from one ormore other retina fundus images. Referring to FIG. 9, captured imagedata and stored image data may be obtained, and a determination may bemade as to whether at least a portion of the stored image data has atleast a predetermined degree of similarity to the captured image data.The captured image data and/or stored image data may be obtained byextraction using a TSC in accordance with any or all embodiments ofFIGS. 5A-5C, 6, and/or 7-8. In the illustrative method of FIG. 10A,template matching is performed between the captured image data and thestored image data to generate a similarity measure. In contrast, theillustrative method of FIG. 10B includes a translationally androtationally invariant feature comparison to generate the similaritymeasure.

FIG. 9 is a flow diagram of illustrative method 900 for identifying aperson by comparing captured image data extracted from a captured imageof the person's retina fundus to stored image data. Method 900 includesobtaining captured image data at step 902, obtaining a portion of storedimage data at step 904, comparing the captured image data to the portionof stored image data at step 906, and determining whether the portion ofstored image data has at least a predetermined degree of similarity tothe captured image data at step 908. If the portion of stored image datais similar enough to constitute a match, method 900 concludes with asuccessful identification (ID). Alternatively, if the portion of storedimage data is not similar enough to constitute a match, method 900continues to step 910 to determine whether there is any stored imagedata which has not yet been compared to the captured image data. If so,method 900 returns to step 904 and obtains a different portion of thestored image data for comparing to the captured image data. If allstored image data has been compared to the captured image data without asuccessful match, method 900 concludes with an unsuccessful ID. Inaccordance with the embodiment of FIG. 1, method 900 may be performed bycomputer 140 using one or more images and/or data transmitted fromdevice 120 a. Alternatively, in accordance with the embodiment of FIG.2, method 900 may be performed entirely by device 120 b.

Obtaining captured image data at step 902 may be performed using imageextraction techniques described in connection with step 304 of FIG. 3.Alternatively or additionally, the captured image data may be outputfrom a TSC in accordance with any or all embodiments of FIGS. 5A-5C, 6,or 7-8. In some embodiments, the captured image data obtained at step902 includes all captured image data acquired for the currentidentification. For example, imaging apparatus 122 a or 122 b maycapture multiple images of the person's retina fundus, and datacorresponding to all retina fundus features of each of the images may beobtained at step 902. Alternatively, data corresponding to only some ofthe images may be obtained at step 902. As a further alternative, datacorresponding to a particular retina fundus feature or set of featuresfor each of the images may be obtained at step 902. Accordingly, in someembodiments, method 900 may return to step 902 to obtain other portionsof the captured image data depending on the result of the comparison atstep 906.

Obtaining stored image data at step 904 may be performed similarly to asdescribed for captured data. The stored image data may be associatedwith one or more previously processed retina fundus images. For example,the stored image data may accumulate as people register with system 100or device 120 b. In some embodiments, registering a person with system100 or device 120 b may include capturing one or more image(s) of theperson's retina fundus, extracting data indicative of features of theperson's retina fundus from the captured image(s), and storing theextracted data on storage medium 142 or 126. In some embodiments,registering the person may include obtaining identification informationsuch as the person's full legal name and government issuedidentification number (e.g., social security number). In someembodiments, the identification information is linked with contactinformation such as the person's telephone number and/or email address.In some embodiments, the person may also provide a username uponregistering. In some embodiments, the stored image data associated witheach registered person may be updated every time system 100 or device120 b successfully identifies the person. For example, when system 100or device 120 b successfully identifies a registered person, thecaptured image(s) used to identify the person may be added to the storedimage data.

As for the captured image data, the stored image data may be processedfrom a 3D volumetric image, a 2D image, fluorescence lifetime data, orOCT spectral data, and may be provided to a TSC. For example, thecaptured image data and stored image data may be provided to a same TSCsuch that extracted feature data from the captured image data and thestored image data may be compared. In some embodiments, the capturedimage data and the stored image data are of the same type. For example,each of the captured and stored image data may include one or more 2Dimages of one or more retinal slices, such as neighboring slices. Whenthe captured and stored image data are associated with a same person,the captured image data may include multiple images of neighboringslices obtained at a first time and the stored image data may includemultiple images of the same neighboring slices obtained at a second timelater than the first time. By way of example, the stored image data mayhave been processed as recently as a few minutes or as long as severalyears before the captured image data is acquired.

In embodiments which provide verification based on biometriccharacteristics in addition to retina fundus identification, one or morerecorded biometric characteristics (e.g., voiceprint, fingerprint, etc.)also may be provided to the TSC in addition to or instead of the retinafundus image(s). In such circumstances, stored characteristic dataassociated with a plurality of biometric characteristics (e.g., forvarious users) may be provided to the TSC. Accordingly, the output(s) ofthe TSC may indicate features of the biometric characteristics tofacilitate comparison of the characteristics in the manner described forretina fundus images. Thus, the TSC may also facilitate verification ofthe identity using biometric characteristics.

As a result of having multiple people registered with system 100 ordevice 120 b, specific portions of the stored image data on storagemedium 142 or 126 may be associated with respective people. Accordingly,obtaining the stored image data at step 904 may include obtaining aportion of the stored image data associated with a registered person.For example, all image data associated with a particular person (e.g.,all data from previous successful identifications) may be obtained atstep 904 for comparing to the captured image data. Alternatively, asingle dataset may be obtained at step 904, for example the most recentimage data acquired for that particular person, and/or data indicatingaspects of a particular retina fundus feature or group of features. Insome embodiments, a single dataset may be acquired at step 904 as acombination of multiple stored datasets, such as an average. In someembodiments, further portions of the stored image data may be obtainedupon a return to step 902 depending on the result of the comparison atstep 906.

Comparing the captured image data to the portion of stored image data atstep 906 may be performed by computer 140 or device 120 b. In accordancewith various embodiments, the comparison may be performed using crosscorrelation, template matching, translationally and rotationallyinvariant maximized weightings, and/or distance metrics. For example, inaccordance with the illustrative embodiment of FIG. 10A, computer 140 ordevice 120 b may perform template matching between the captured imagedata obtained at step 902 and the stored image data at step 904 togenerate a similarity measure. Alternatively, in accordance with theillustrative embodiment of FIG. 10B, computer 140 or device 120 b maycompare relative positions and/or orientations of translationally androtationally invariant features of the captured image data obtained atstep 902 and the stored image data obtained at step 904 to generate thesimilarity measure. The comparison at step 906 may compare data for allretina fundus features, or only for individual or groups of features.For example, separate comparisons may be made between aspects of anoptic disc in the captured image data and in the stored image data, andaspects of blood vessels such as branch endings or bifurcations of thecaptured image data and the stored image data. A comparison of oneaspect may be made at step 906 in one instance, and method 900 may latercircle back to step 906 to perform another comparison for a differentaspect.

Determining whether the portion of stored image data and the capturedimage data have at least a predetermined degree of similarity at step908 may be based on the similarity measure generated at step 906. Forexample, the similarity measure may provide the degree of similaritybetween the two datasets, and step 908 may include determining whetherthe degree of similarity provided by the similarity measure meets thepredetermined degree of similarity used as a threshold for a successfulidentification.

The predetermined degree of similarity may be set based on a number offactors, such as the number of captured images from which the capturedimage data is extracted, the resolution and field of view of the images,the number of different types of features indicated in the capturedimage data and the stored image data, and the comparison techniqueimplemented at step 906. While the predetermined degree of similarityshould be set relatively high to prevent fraudulent identification, sucha high predetermined degree of similarity could result in a high falserejection ratio, making it more difficult to positively identify thecorrect person. Generally, the predetermined degree of similarity may beas high as the number of images, the resolution and field of view of theimage(s), and the number of different types of features used all permit.For example, a large number of high quality captured images with manydifferent types of features facilitate use of a higher predetermineddegree of similarity without risking a high false rejection ratio. Thisis because there is a greater amount of information in the captureddata, which may lessen the impact of imperfections in the captured image(e.g., poor lighting) or in the transmitted data (e.g., due to errors intransmission).

If the portion of stored image data has at least the predetermineddegree of similarity to the stored image data, method 900 may concludewith a successful match. In some embodiments, computer 140 or device 120b may obtain identification information associated with the portion ofstored image data from storage medium 142 or 126. Alternatively,computer 140 or device 120 b

may obtain the identification information from another location oncommunication network 160. For example, the identification informationmay be stored together with the portion of stored image data, or thestored image data may include a link to a location where theidentification information is stored. In some embodiments, theidentification information may include the person's full name and/orusername.

In embodiments in which biometric verification is performed based onrecorded biometric characteristics, comparison between captured andstored biometric characteristic data may be conducted in the mannerdescribed for retina fundus images and image data. Biometricverification is typically performed after identification information isobtained. For example, the stored biometric characteristic data may bestored with the identification information. As a result, the biometriccharacteristic comparison may be performed after the retina fundusidentification is complete. In embodiments which use a TSC, the storedbiometric characteristic data may be provided as an input to the TSC atthe same time as the recorded biometric characteristic data, oralternatively afterwards. For example, the recorded biometriccharacteristic data may be provided to the TSC at the same time or evenbefore the identification, with the output(s) of the TSC being saved foruse after the identification is complete.

In accordance with the embodiment of FIG. 1, computer 140 may obtain andtransmit the identification information to device 120 a to concludeidentifying the person. Device 120 a or 120 b may notify the person thatthe identification was successful, for example via a user interfacegenerated on one or more displays. In some embodiments, device 120 a or120 b may grant the person access to health information or an accountassociated with the person, as described herein including with referenceto section III. In some embodiments, the stored image data may beupdated to include some or all of the captured image data, such as dataindicating retina fundus features, for future identifications.

If the portion of stored image data does not have at least thepredetermined degree of similarity to the captured image data, method900 proceeds to step 910 to determine whether there is more stored imagedata which has not yet been compared to the captured image data. Ifthere is more stored image data which has not yet been compared to thecaptured image data, method 900 returns to step 904 and obtains aportion of the stored image data which has not yet been compared. Forexample, each portion of the stored image data compared to the capturedimage data may be associated with a registered person, and a portion ofthe remaining stored image data could still match the captured imagedata to identify the person. It should be appreciated that, in someembodiments, method 900 may return to step 902 rather than step 904. Forexample, the captured image data may include multiple portionscorresponding to multiple captured images of the person's retina fundus,and so a different portion corresponding to one or more other capturedimages may be obtained at step 902 for comparing against the same storedimage data previously obtained at step 904.

Alternatively, if there is no more stored image data to compare to thecaptured image data, method 900 may conclude with an unsuccessfulidentification. For example, the captured image data may correspond to aperson who has not yet registered with system 100 or device 120 b. Inaccordance with the embodiment of FIG. 1, computer 140 may notify device120 a of the unsuccessful identification, and device 120 a may promptthe person to register with system 100, for example by providingidentification information which may be stored with the captured imagedata. In accordance with the embodiment of FIG. 2, device 120 b mayprompt the person to register with device 120 b. It should beappreciated that device 120 a and 120 b may not be configured toregister a new user, for example in embodiments of system 100 and device120 b which may be configured to only register a new user in thepresence of a healthcare professional.

FIG. 10A is a flow diagram of illustrative method 1000 a for comparingcaptured image data to stored image data by template matching. Method1000 a includes performing template-matching at step 1002 a, andgenerating a similarity measure at step 1004 a. In some embodiments,method 1000 a may be performed by device 120 b or computer 140. In someembodiments, method 1000 a may be performed for each subset of datastored on storage medium 142 or storage medium 126 corresponding to asingle image, or to a combination of images associated with a sameperson.

Performing template-matching at step 1002 a may include device 120 b orcomputer 140 comparing at least a portion of the captured image dataobtained at step 902 of method 900 to at least a portion of the storedimage data obtained at step 904. For example, a portion of the capturedimage data corresponding to a region of the image(s) captured by imagingapparatus 122 a or 122 b may be compared against one or more portions ofthe stored image data corresponding to a region of one or more imagesfrom which the stored image data was extracted. During such comparison,a cross-correlation such as by convolution or other multiplication maybe performed between the portion of the captured image data and theportion(s) of the stored image data. In some embodiments, the comparisonincludes matrix multiplication with the result being stored in asimilarity matrix. The similarity matrix may be used at step 1004 a forgenerating a similarity measure.

In some instances, the portion of the captured image(s) may be comparedagainst the portion(s) of the stored image data, and then may be resizedand/or rotated and compared against the same portion(s). The portion ofthe captured image data may then be compared against one or more otherportions of the stored image data corresponding to other regions of theimage(s) from which the stored image data was extracted. In embodimentswhere the stored image data is associated with multiple images, once theportion of the captured image data has been compared to all of thestored image data associated with a particular image, the portion of thecaptured image data may be compared to stored image data associated witha different image. Alternatively, a separate comparison may be performedfor individual retina fundus features or groups of features acrossmultiple images. Once the portion of the captured image data has beencompared to all of the stored image data associated with a particularperson, for example all images of the person or all data indicatingvarious features from the images, method 1000 a may proceed togenerating a similarity measure at step 1004 a corresponding to theparticular person. For example, the similarity measure may indicatewhether or not the captured image matches the particular person.

Generating a similarity measure at step 1004 a may include device 120 bor computer 140 calculating similarity between the captured image dataobtained at step 902 of method 900 and the stored image data obtained atstep 904. In some embodiments, a separate similarity measure may becalculated between the captured image data and each portion of thestored image data associated with a particular image. In someembodiments, a single similarity measure may be calculated between thecompared image data and the entirety of the stored image data. Forexample, the similarity measure may be a maximum degree of similaritycalculated between the captured image data and the stored data.Alternatively, the similarity measure may be average similarity betweenthe captured image data and various portions of the stored image data.In embodiments in which comparing the captured image data to the storedimage data includes performing a convolution resulting in a similaritymatrix, portions of the similarity measure may be generated duringcomparison, and the similarity measure may be finalized to account forall comparison data once template-matching is complete.

FIG. 10B is a flow diagram of illustrative method 1000 b for comparingtranslationally and rotationally invariant features indicated in thecaptured image data to those indicated in stored image data. Forexample, the translationally and rotationally invariant features may beindicated in the output of a TSC in accordance with the embodiments ofFIGS. 5A-5C, 6, and 7-8. Method 1000 b includes performing atranslationally and rotationally invariant feature comparison at step1002 b, and generating a similarity measure at step 1004 b. Method 1000b may be performed by device 120 b or computer 140.

Performing the translationally and rotationally invariant featurecomparison at step 1002 b may include device 120 b, computer 140comparing relative positions and orientations of translationally androtationally invariant features indicated in the captured image data torelative positions and orientations of translationally and rotationallyinvariant features indicated in the stored image data. For example, aSIFT or SURF comparison may be performed between some or all of thecaptured image data and the stored image data. In embodiments where thestored image data is associated with multiple images, separatecomparisons may be performed for each portion of the stored image dataassociated with a particular image. Alternatively, in some embodiments,separate comparison may be performed for portions of the stored dataindicating a particular retina fundus feature or group of features, forexample including data associated with multiple images indicating theparticular feature(s) in the multiple images. In some instances, thefeature data may be combined from the multiple images and comparedagainst the captured image data.

Generating a similarity measure at step 1004 b may be conducted in themanner described for step 1004 a in connection with FIG. 10A. Forexample, a similarity measure may be generated for each portion of thestored image data compared to the captured image data. Alternatively, asingle similarity measure may be generated based on comparing portionsof the stored image data associated with multiple images of a sameperson and/or focusing on different retina fundus features in eachcomparison, such that a similarity measure is generated for each imageor for each particular feature or group of features.

III. Techniques for Accessing Electronic Records or Devices of a PersonBased on a Retinal Image of the Person

The inventors have developed techniques for securing and/or accessingelectronic accounts or records or devices associated with a person witha biometric security system configured to enable access based on animage of the person's retina fundus. As one example, the inventors havedeveloped techniques for securing a user account or a device usingbiometric identification. Further, the inventors have developedtechniques for securing health information such as electronic healthrecords associated with a person using biometric identification.Techniques for biometric identification may also be useful in othercontexts of identifying a person, such as to secure a financialtransaction. The biometric identification includes enabling accessthrough identification of the person based on a retinal image and/orretinal measurement of the person. This retinal image and/or measurementof the person may be obtained through use of at least one of OCT andFLIO.

In some embodiments of FIGS. 1-2, device 120 a or 120 b may beconfigured to grant a person access to device 120 a or 120 b upon asuccessful identification. For example, device 120 a may grant theperson access upon receiving notification of a successful identificationfrom computer 140. In some embodiments, device 120 a may receive useraccount data specific to the person along with the notification. Forexample, device 120 a may receive personalized settings from computer140, such as a preferred audio/visual theme (e.g., a color theme and/orsounds), graphics settings (e.g., colorblind preferences), apersonalized home screen (e.g., desktop background), and/or softwareapplications previously accessed by the person for operating device 120a. In some embodiments, device 120 b may have personalized settingsstored on storage medium 126, and may select the personalized settingsspecific to the person upon successful identification. Alternatively oradditionally, device 120 a or device 120 b may be configured to grantthe person access to various other types of accounts such as a socialmedia account on the internet, and/or a financial account for conductinga transaction.

In some embodiments of FIGS. 1-2, device 120 a or 120 b may beconfigured to provide access to health information such as electronichealth records upon a successful identification. For example, computer140 may store health information associated with one or more people, andupon successfully identifying a person, may transmit health informationassociated with the person to device 120 a. Alternatively, device 120 bmay store the health information thereon, which may be obtained, forexample from storage medium 126, upon successfully identifying theperson. In some embodiments, device 120 a or 120 b, or computer 140 mayupdate the health information based on retina fundus features indicatedin the captured image(s). For example, in some embodiments, the healthinformation may be updated to include the captured image(s) and/orfeature data extracted therefrom during identification or otherwise. Inthis way, health information may be updated each time the person logsinto device 120 a or 120 b. In some embodiments, the person may updateelectronic health records by reporting symptoms the person isexperiencing directly into their electronic health records using device120 a or 120 b rather than frequently having to meet in person withtheir healthcare professional.

FIG. 11 is a block diagram illustrating exemplary user interface 1100 inaccordance with the embodiments of FIGS. 1-2. For example, userinterface 1100 is provided on display 1130, which may be a display ofdevice 120 a or 120 b.

Display 1130 may be a liquid crystal display (LCD) screen such as acomputer monitor or phone screen, or alternatively may be a projectionor hologram. In some embodiments, display 1130 may include a touchscreenconfigured for user interaction by pressing content which appears on thetouchscreen. In some embodiments, display 1130 may be integrated withdevice 120 a or 120 b. Alternatively, in some embodiments, display 1130may be separate from device 120 a or 120 b and may be coupled through awired or wireless connection to device 120 a or 120 b.

Display 1130 includes portions for identification information 1132,health information 1134, financial information 1136, and otherinformation 1138 on display 1130. In some embodiments, identificationinformation 1132, health information 1134, and/or financial information1136 may appear at edges of display 1130 while other information 1138 ispresented to a user. As a non-limiting example, identificationinformation 1132 may include a person's username, health information1134 may include the person's stress level, financial information 1136may include the person's bank account balance, and other information1138 may include a message received over social media.

In some embodiments, identification information 1132 may indicate to auser whether an identification was successful. For example,identification information 1132 may include a notification indicating asuccessful identification. Alternatively or additionally, identificationinformation 1132 may include the name of the identified person obtainedusing biometric identification.

In some embodiments, health information 1134, financial information,and/or other information 1138 may be obtained during or in addition tobiometric identification. In some embodiments, device 120 a or 120 b maybe configured to access and/or update health information associated withthe person upon successful identification. Alternatively oradditionally, device 120 a or 120 b may be configured to access and/orupdate financial or other account information associated with the personupon successful identification.

Health information 1134 may be obtained from computer 140 in accordancewith the embodiment of FIG. 1 or from storage medium 126 of device 120 bin accordance with the embodiment of FIG. 2. In some embodiments, healthinformation 1134 may include a notification with a health warning, forexample, based on information obtained from computer 140 or storagemedium 126. Health information 1134 may include risk assessmentsassociated with diabetes, cardiovascular disease, concussion,Parkinson's disease, Alzheimer's disease, and/or stress. In someembodiments, the health information may alternatively or additionallyinclude risk assessments specific to the person's retina health. Forexample, the risk assessments may be associated with diabeticretinopathy, age-related macular degeneration, macular edema, retinalartery occlusion, retinal nerve-fiber layer, and/or glaucoma.

Financial information 1136 may be obtained from computer 140 inaccordance with the embodiment of FIG. 1 or from storage medium 126 ofdevice 120 b in accordance with the embodiment of FIG. 2. In someembodiments, financial information 1136 may include balances for one ormore financial accounts associated with the person such as banking orinvestment accounts.

It should be appreciated that display 1130 may include only some ofidentification information 1132, health information 1134, financialinformation 1136, and/or other information 1138, as this example merelydemonstrates how a user may interact with multiple forms of informationin accordance with various embodiments.

Patients typically access and/or update their electronic health recordsby consulting their healthcare professionals in person or through anonline database accessible with a password or passcode. As described insection II, the inventors have recognized that biometric securitysystems configured to identify a person using a captured image of theperson's retina fundus as described herein provide enhanced protectionbeyond passwords and passcodes while achieving lower false rejection andfalse acceptance rates than existing biometric security systems.Security and confidentiality of patients' health information is animportant consideration when making patients' health information moreaccessible and easy for patients to update by themselves. If electronichealth records are left unsecured or inadequately secured, parties otherthan patients and their healthcare professionals may be able to accesssensitive health information. The resulting lack of confidentiality maycause patients to lose trust that their information is private, and maybe further dissuaded from seeking medical attention. In addition, ifpatients' electronic health records could be forged or otherwisefraudulently altered, healthcare professionals would not be able to makeproper diagnoses. Accordingly, the inventors have developed systems foraccessing health information securely using biometric identificationsystems, such that health information may be more accessible to patientswhile maintaining confidentiality and security.

In some embodiments, device 120 a or device 120 b may be configured toidentify a person and access the person's electronic health records,even if the person is unconscious. For example, during a mass casualtyevent such as a natural disaster, unconscious victims may be identifiedand their electronic health records may be obtained using device 120 aor device 120 b. For example, a first responder such as an EmergencyMedical Technician (EMT) may use the device to identify each person andto access health information using the device in order to moreaccurately conduct triage. Thus, the device may facilitate responding toevents such as natural disasters in a quick and organized fashion.

Referring to FIG. 12, health or other account information may be storedon one or more components of a distributed ledger such as a blockchain.The inventors have recognized that a distributed ledger offers aconcrete record of changes made to data stored on the ledger. Forexample, each component of the ledger may have a unique identifier whichis updated to reflect a time and/or scope of changes made to thecomponent, and/or changes made to other components within the ledger.Accordingly, a distributed ledger may facilitate detecting whetherinformation stored on the ledger, such as identification information,user account data, financial data, or health information, has beenchanged, as well as when and to what extent changes were made. Theinventors have recognized that securing access to components of adistributed ledger for electronic health records with a biometricidentification system enhances the accuracy and confidentiality of theelectronic health records. In some embodiments, changes to healthinformation stored on the distributed ledger may only be made by theperson with whom the health information is associated, or an authorizedhealthcare professional such as the person's doctor.

In accordance with various embodiments, components of a distributedledger may include user account data, financial data, health informationsuch as electronic health records, stored image data and/oridentification information associated with the person or others.

FIG. 12 is a block diagram illustrating exemplary distributed ledger1200 including components 1220 and 1240 accessible over network 1260.Distributed ledger 1200 may implement a distributed data structure withcomponent(s) 1220 and 1240 of the ledger being stored on various devicesand computers such as device 120 a, device 120 b, or computer 140, andaccessible over communication network 160. For example, in someembodiments, network 1260 may be communication network 160 of FIG. 1,such that components 1220 and 1240 may be stored on or may be accessibleto device 120 a and/or computer 140. Alternatively, network 1260 may bea sub-network of communication network 160, such as a peer-to-peer (P2P)network distributed across communication network 160 but not accessibleto all devices on communication network 160. According to a non-limitingexample, distributed ledger 1200 may implement a blockchain, withcomponents 1220 and 1240 serving as blocks with block headers linked toother blocks in the chain.

Component 1220 includes header 1222 and data 1224, and component 1240includes header 1242 and data 1244. In accordance with variousembodiments, data 1224 and/or 1244 may include stored image data, healthinformation such as electronic health records, user account data,financial data, and/or identification information associated with aperson. Headers 1222 and 1242 may each include a unique identifierspecific to component 1220 and 1240, such as an address or hash foridentifying component 1220 or 1240. The identifier may includeinformation referring back and/or forward to one or more othercomponents in the chain. For example, if component 1220 and 1240 arelinked, header 1222 may include information referring to component 1240,and/or header 1242 may include information referring to component 1220.Alternatively or additionally, the identifier may include informationbased on changes made to data 1224 or 1244 of each component, such asthe time or extent to which the changes were made. In some embodiments,the identifier may result from a mathematical operation involvingidentifiers of other components and/or information associated withchanges to the data of the component. For example, data 1224 ofcomponent 1220 may include a person's identification information and/orelectronic health records, which may be changed to include updatedhealth information. Accordingly, header 1222 may be updated to indicatethat changes were made, and in some cases, the scope of the changes. Inaddition, headers of other components linked to component 1220 may alsobe updated to include the updated identifier of the component 1220. Forexample, in some embodiments where component 1240 is linked to component1220, header 1242 may be updated based on changes to header 1222 and/orvice versa.

In the embodiments of FIGS. 1-2, device 120 a and/or computer 140 may,at times, store one or more components of the distributed ledger.Alternatively or additionally, device 120 a and/or computer 140 may beconfigured to access component(s) 1220 and/or 1240 of distributed ledger1200 having data 1224 and/or 1244 associated with the person.

In the embodiments of FIGS. 1-10B, biometric identification may beperformed using stored image data from components 1220 and/or 1240 ofdistributed ledger 1200. For example, device 120 b or computer 140 mayobtain the stored image data from component(s) 1220 and/or 1240 ofdistributed ledger 1200. Further, identification information may bestored as at least a portion of data 1224 and/or 1244 of components 1220and/or 1240. In some embodiments, data 1224 of component 1220 mayinclude stored image data, as well as a link to component 1240 which maystore identification information associated with the stored image datain data 1244. Upon determining that stored image data on component 1220has at least the predetermined degree of similarity to the capturedimage data, identification information associated with the person may beobtained from component 1220 having the stored image data, or may beobtained from linked component 1240.

IV. Techniques for Determining a Health Status of a Person Based on aRetinal Image of the Person

The inventors have developed techniques for using a captured image of aperson's retina fundus to determine the person's predisposition tocertain diseases. For example, the appearance the person's retina fundusmay indicate whether the person is at risk for various conditions suchas diabetes, an adverse cardiovascular event, or stress, as describedherein. As an advantage of integrating health status determination intoa system for biometric identification, captured image data foridentifying the person may be used to determine the person's healthstatus. In accordance with various embodiments, the determination of theperson's predisposition based on images of the person's retina fundusmay be performed before, during, or after identifying the person. Forexample, the determination may be performed separately from theidentification, or may be performed as an additional or alternative stepduring the identification.

The inventors have recognized that various medical conditions may beindicated by the appearance of a person's retina fundus. For example,diabetic retinopathy may be indicated by tiny bulges or micro-aneurysmsprotruding from the vessel walls of the smaller blood vessels, sometimesleaking fluid and blood into the retina. In addition, larger retinalvessels can begin to dilate and become irregular in diameter. Nervefibers in the retina may begin to swell. Sometimes, the central part ofthe retina (macula) begins to swell, such as macular edema. Damagedblood vessels may close off, causing the growth of new, abnormal bloodvessels in the retina. Glaucomatous optic neuropathy, or Glaucoma, maybe indicated by thinning of the parapapillary retinal nerve fiber layer(RNFL) and optic disc cupping as a result of axonal and secondaryretinal ganglion cell loss. The inventors have recognized that RNFLdefects, for example indicated by OCT, are one of the earliest signs ofglaucoma. In addition, age-related macular degeneration (AMD) may beindicated by the macula peeling and/or lifting, disturbances of macularpigmentation such as yellowish material under the pigment epitheliallayer in the central retinal zone, and/or drusen such as macular drusen,peripheral drusen, and/or granular pattern drusen. AMD may also beindicated by geographic atrophy, such as a sharply delineated round areaof hyperpigmentation, nummular atrophy, and/or subretinal fluid.Stargardt's disease may be indicated by death of photoreceptor cells inthe central portion of the retina. Macular edema may be indicated by atrench in an area surrounding the fovea. A macular hole may be indicatedby a hole in the macula. Eye floaters may be indicated by non-focusedoptical path obscuring. Retinal detachment may be indicated by severeoptic disc disruption, and/or separation from the underlying pigmentepithelium. Retinal degeneration may be indicated by the deteriorationof the retina. Central serous retinopathy (CSR) may be indicated by anelevation of sensory retina in the macula, and/or localized detachmentfrom the pigment epithelium. Choroidal melanoma may be indicated by amalignant tumor derived from pigment cells initiated in the choroid.Cataracts may be indicated by opaque lens, and may also cause blurringfluorescence lifetimes and/or 2D retina fundus images. Maculartelangiectasia may be indicated by a ring of fluorescence lifetimesincreasing dramatically for the macula, and by smaller blood vesselsdegrading in and around the fovea. Alzheimer's disease and Parkinson'sdisease may be indicated by thinning of the RNFL. It should beappreciated that diabetic retinopathy, glaucoma, and other suchconditions may lead to blindness or severe visual impairment if notproperly screened and treated.

Accordingly, in some embodiments, systems and devices described hereinmay be configured to determine the person's predisposition to variousmedical conditions based on one or more images of the person's retinafundus. For example, if one or more of the above described signs of aparticular medical condition (e.g., macula peeling and/or lifting forage-related macular degeneration) is detected in the image(s), thesystem and/or device may determine that the person is predisposed tothat medical condition. In such situations, the system or device maynotify the person directly and/or may notify the person's healthprofessional of the person's predisposition.

Furthermore, in some embodiments, systems and devices described hereinmay make such medical predisposition determinations based on capturedand stored images. For example, some signs such as thinning of the RNFLmay be indicated by comparison of the captured image(s) to the storedimages when identifying the person. While such a progression would posea challenge for existing identification systems as it may result in afalse rejection of the correct person, systems described herein may beconfigured to account for such differences upon determination of theperson's medical condition. Thus, the inventors have developed systemsand devices which not only detect signs of and determine a person'smedical condition, but also adapt to account for the medical conditionduring identification.

Alternatively or additionally, in some embodiments, systems and devicesdescribed herein may make such medical predisposition determinationsbased on one or more outputs from a TSC. For example, one or more imagesof a person's retina fundus may be provided as an input to the TSC,which may provide one or more outputs indicative of features of theperson's retina fundus. In some embodiments, each output may indicate alikelihood of a sign of a medical condition being in a particularportion of a particular image. Alternatively, one or more outputs mayindicate a likelihood of a sign of multiple medical conditions in asingle or multiple images. Further, the output(s) may indicate thelikelihood of multiple signs of one or of multiple medical conditions ina single or multiple images. The output(s) may indicate the likelihoodof one or more signs of one or more medical conditions being presentacross multiple locations in a single or in multiple images.Accordingly, a determination of the person's predisposition to variousmedical conditions may be made based on the output(s) from the TSC. Whenstored image data is also provided as input to the TSC, the output(s)from the TSC may not only be used to identify the person as describedherein, but also to make medical condition determinations based on thefeatures indicated in the output(s).

In some embodiments, upon a successful identification, risk assessmentsin the person's health information may be updated based on theappearance of retina fundus features in the captured image data. Forexample, in accordance with the embodiment of FIG. 1, the riskassessments may be updated on computer 140 and/or may be provided todevice 120 a for display in user interface 1100 of FIG. 11. Inaccordance with the embodiment of FIG. 2, the risk assessments may beupdated on device 120 b and/or may be provided for display in userinterface 1100.

V. Techniques for Diagnosing a Health Condition of a Person Based on aRetinal Image of the Person

The inventors have also developed techniques for using a captured imageof a person's retina fundus to diagnose various health conditions ordiseases of the person. For example, in some embodiments, any of thehealth conditions described in section IV may be diagnosed beforeidentification, during identification, after a successfulidentification, and/or using data accumulated during one or moreidentifications. Alternatively or additionally, such conditions mayinclude retinoblastoma, or correctable vision problems such asnearsightedness or amblyopia. Such determinations may be performed inthe manner described in section IV. In accordance with the embodiment ofFIG. 1, computer 140 may perform the diagnosis and provide the resultsof the diagnosis to device 120 a. In accordance with the embodiment ofFIG. 2, device 120 b may perform the diagnosis and provide the resultsof the diagnosis thereon. In some embodiments, the results of thediagnosis may be alternatively or additionally provided to a healthcareprofessional, such as the person's doctor.

VI. Applications

As described, a captured image of a person's retina fundus can be usedto identify the person, access an electronic record or secure device ofthe person, determine a health status of the person (includingdetermining the person's propensity to obtaining certain diseases orconditions), and/or diagnose an actual disease or health condition (suchas Alzheimer's, diabetes, certain autoimmune disorders, etc.) of theperson. In addition, systems and devices described herein may beconfigured to determine a person's vital signs, blood pressure, heartrate, and/or red and white blood cell counts. Further, systems anddevices described herein may be configured for use with other medicaldevices such as ultrasound probes, magnetic resonance imaging (MRI)systems, or others. Examples of ultrasound probes for use with systemsand devices as described herein are described in U.S. Pat. ApplicationNo. 2017/0360397, titled “UNIVERSAL ULTRASOUND DEVICE AND RELATEDAPPARATUS AND METHODS”, which is herein incorporated by reference in itsentirety. Examples of MRI systems for use with systems and devices asdescribed herein are described in U.S. Pat. Application No.2018/0164390, titled “ELECTROMAGNETIC SHIELDING FOR MAGNETIC RESONANCEIMAGING METHODS AND APPARATUS”, which is herein incorporated byreference in its entirety.

Having thus described several aspects and embodiments of the technologyset forth in the disclosure, it is to be appreciated that variousalterations, modifications, and improvements will readily occur to thoseskilled in the art. Such alterations, modifications, and improvementsare intended to be within the spirit and scope of the technologydescribed herein. For example, those of ordinary skill in the art willreadily envision a variety of other means and/or structures forperforming the function and/or obtaining the results and/or one or moreof the advantages described herein, and each of such variations and/ormodifications is deemed to be within the scope of the embodimentsdescribed herein. Those skilled in the art will recognize, or be able toascertain using no more than routine experimentation, many equivalentsto the specific embodiments described herein. It is, therefore, to beunderstood that the foregoing embodiments are presented by way ofexample only and that, within the scope of the appended claims andequivalents thereto, inventive embodiments may be practiced otherwisethan as specifically described. In addition, any combination of two ormore features, systems, articles, materials, kits, and/or methodsdescribed herein, if such features, systems, articles, materials, kits,and/or methods are not mutually inconsistent, is included within thescope of the present disclosure.

The above-described embodiments can be implemented in any of numerousways. One or more aspects and embodiments of the present disclosureinvolving the performance of processes or methods may utilize programinstructions executable by a device (e.g., a computer, a processor, orother device) to perform, or control performance of, the processes ormethods. In this respect, various inventive concepts may be embodied asa computer readable storage medium (or multiple computer readablestorage media) (e.g., a computer memory, one or more floppy discs,compact discs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement one or more of the variousembodiments described above. The computer readable medium or media canbe transportable, such that the program or programs stored thereon canbe loaded onto one or more different computers or other processors toimplement various ones of the aspects described above. In someembodiments, computer readable media may be non-transitory media.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects as described above. Additionally,it should be appreciated that according to one aspect, one or morecomputer programs that when executed perform methods of the presentdisclosure need not reside on a single computer or processor, but may bedistributed in a modular fashion among a number of different computersor processors to implement various aspects of the present disclosure.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

When implemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer, as non-limitingexamples. Additionally, a computer may be embedded in a device notgenerally regarded as a computer but with suitable processingcapabilities, including a Personal Digital Assistant (PDA), a smartphoneor any other suitable portable or fixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audibleformats.

Such computers may be interconnected by one or more networks in anysuitable form, including a local area network or a wide area network,such as an enterprise network, and intelligent network (IN) or theInternet. Such networks may be based on any suitable technology and mayoperate according to any suitable protocol and may include wirelessnetworks, wired networks or fiber optic networks.

Also, as described, some aspects may be embodied as one or more methods.In some embodiments, methods may incorporate aspects of one or moretechniques described herein.

For example, FIG. 13A is a flow diagram illustrating exemplary method1300 a including transmitting, over a communication network [e.g., tothe cloud], first image data associated with and/or including a firstimage of a person's retina fundus at step 1320 a, and receiving, overthe communication network, an identity of the person at step 1340 a, inaccordance with some or all of the embodiments described herein.

FIG. 13B is a flow diagram illustrating exemplary method 1300 bincluding, based on first image data associated with and/or including afirst image of a person's retina fundus, identifying the person at step1320 b, and, based on a first biometric characteristic of the person,verifying an identity of the person at step 1340 b, in accordance withsome or all of the embodiments described herein. It should beappreciated that, in some embodiments, step 1320 a may alternatively oradditionally include identifying the person based on a first of multipletypes of features indicated in the first image data, and/or 1340 b mayinclude verifying the identity based on a second of the multiple typesof features.

FIG. 13C is a flow diagram illustrating exemplary method 1300 cincluding, based on first image data associated with and/or including afirst image of a person's retina fundus, identifying the person at step1320 c and updating stored data associated with a plurality of retinafundus images at step 1340 c, in accordance with some or all embodimentsdescribed herein.

FIG. 13D is a flow diagram illustrating exemplary method 1300 dincluding providing, as a first input to a trained statisticalclassifier (TSC), first image data associated with and/or including afirst image of a person's retina fundus at step 1320 d and, based on atleast one output from the TSC, identifying the person at step 1340 d, inaccordance with some or all embodiments described herein.

FIG. 13E is a flow diagram illustrating exemplary method 1300 eincluding, based on first image data associated with and/or including afirst image of a person's retina fundus, identifying the person at step1320 e and determining a medical condition of the person at step 1340 e,in accordance with some or all embodiments described herein.

FIG. 13F is a flow diagram illustrating exemplary method 1300 gincluding providing, as a first input to a trained statisticalclassifier (TSC), first image data associated with and/or including afirst image of a person's retina fundus at step 1320 f, based on atleast one output from the TSC, identifying the person at step 1340 f,and determining a medical condition of the person at step 1360 f, inaccordance with some or all embodiments described herein.

The acts performed as part of the methods may be ordered in any suitableway. Accordingly, embodiments may be constructed in which acts areperformed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” “composed of,” and the like are tobe understood to be open-ended, i.e., to mean including but not limitedto. Only the transitional phrases “consisting of” and “consistingessentially of” shall be closed or semi-closed transitional phrases,respectively.

What is claimed is:
 1. An imaging apparatus, comprising: a housingconfigured to accommodate a first eye and a second eye of a person, thehousing having disposed therein: an optical coherence tomography (OCT)device configured for imaging and/or measuring a retina of the firsteye; and a fluorescence device configured for imaging and/or measuring aretina of the second eye.
 2. An imaging apparatus, comprising: abinocular-shaped housing having disposed therein a plurality of imagingdevices including an optical imaging device and a fluorescence imagingdevice, wherein the optical imaging device and the fluorescence imagingdevice are configured to perform imaging and/or measuring using a sameoptical component.
 3. The imaging apparatus of claim 2, wherein the sameoptical component includes a lens.
 4. The imaging apparatus of claim 2,wherein the optical imaging device includes an optical coherencetomography (OCT) device.
 5. The imaging apparatus of claim 2 wherein thefluorescence imaging device includes a fluorescence lifetime imagingdevice.
 6. The imaging apparatus of claim 2, further including at leastone processor configured to identify a person wearing the imagingapparatus based on at least one image captured using the plurality ofimaging devices.
 7. The imaging apparatus of claim 6, wherein the atleast one processor is configured to: input the at least one image to atrained statistical classifier (TSC); and obtain, as an output from theTSC, an identity of the person.
 8. The imaging apparatus of claim 6,wherein the at least one processor is configured to: transmit, over acommunication network to a device, the at least one image; and receive,over the communication network from the device, an identity of theperson.
 9. The imaging apparatus of claim 8, wherein the at least oneprocessor is further configured to access, over the communicationnetwork, health information associated with the person.
 10. The imagingapparatus of claim 6, wherein the at least one processor is furtherconfigured to approve security access for the person based onidentifying the person.
 11. The imaging apparatus of claim 6, whereinthe at least one processor is further configured to determine a healthstatus of the person based on the at least one image.
 12. The imagingapparatus of claim 9, wherein the at least one processor is configuredto: input the at least one image to a trained statistical classifier(TSC); and obtain, as an output from the TSC, the health status of theperson.
 13. The imaging apparatus of claim 6, wherein the at least oneprocessor is further configured to determine a medical condition of theperson based on the at least one image.
 14. The imaging apparatus ofclaim 13, wherein the at least one processor is configured to: input theat least one image to a trained statistical classifier (TSC); andobtain, as an output from the TSC, the medical condition of the person.15. The imaging apparatus of claim 2, further including circuitryconfigured to transmit, over a communication network, at least one imagecaptured using the plurality of imaging devices.
 16. An imagingapparatus, comprising: a housing having a plurality of imaging devicesand at least two lenses disposed therein, wherein the at least twolenses are respectively aligned with at least two of the plurality ofimaging devices for imaging and/or measuring at least one of first andsecond eyes of a person.
 17. The imaging apparatus of claim 16, whereinthe housing includes a portion configured to accommodate the first andsecond eyes of the person for aligning the first and second eyes withthe at least two lenses.
 18. The imaging apparatus of claim 16, whereinthe at least two lenses include: a first lens positioned for aligningwith the first eye and a first imaging device of the at least two of theplurality of imaging devices; and a second lens positioned for aligningwith the second eye and a second imaging device of the at least two ofthe plurality of imaging devices.
 19. The imaging apparatus of claim 18,wherein the first imaging device includes an optical coherencetomography (OCT) device and the second imaging device includes afluorescence lifetime device.
 20. The imaging apparatus of claim 16,further including at least one processor configured to identify a personwearing the imaging apparatus based on at least one image captured usingthe plurality of imaging devices.
 21. The imaging apparatus of claim 20,wherein the at least one processor is configured to: input the at leastone image to a trained statistical classifier (TSC); and obtain, as anoutput from the TSC, an identity of the person.
 22. The imagingapparatus of claim 20, wherein the at least one processor is configuredto: transmit, over a communication network to a device, the at least oneimage; and receive, over the communication network from the device, anidentity of the person.
 23. The imaging apparatus of claim 20, whereinthe at least one processor is further configured to access, over thecommunication network, health information associated with the person.24. The imaging apparatus of claim 20, wherein the at least oneprocessor is further configured to approve security access for theperson based on identifying the person.
 25. The imaging apparatus ofclaim 20, wherein the at least one processor is further configured todetermine a health status of the person based on the at least one image.26. The imaging apparatus of claim 25, wherein the at least oneprocessor is configured to: input the at least one image to a trainedstatistical classifier (TSC); and obtain, as an output from the TSC, thehealth status of the person.
 27. The imaging apparatus of claim 20,wherein the at least one processor is further configured to determine amedical condition of the person based on the at least one image.
 28. Theimaging apparatus of claim 27, wherein the at least one processor isconfigured to: input the at least one image to a trained statisticalclassifier (TSC); and obtain, as an output from the TSC, the medicalcondition of the person.
 29. The imaging apparatus of claim 16, furtherincluding circuitry configured to transmit, over a communicationnetwork, at least one image captured using the plurality of imagingdevices.
 30. A stand for an imaging apparatus, the stand comprising: aholding portion configured to receive a housing of the imagingapparatus; and a base coupled to the holding portion and configured tosupport the imaging apparatus when received in the holding portion. 31.The stand of claim 30, wherein the stand further comprises a wirelesscharging device configured to provide power to the imaging apparatuswhen the imaging apparatus is received in the holding portion.
 32. Thestand of claim 31, wherein the wireless charging device comprises apower supply and at least one wireless charging coil configured totransmit the power wirelessly to the imaging apparatus when the imagingapparatus is received in the holding portion.
 33. The stand of claim 32,wherein the holding portion houses the wireless charging device.
 34. Thestand of claim 32, wherein the power supply comprises a rechargeablebattery.
 35. The stand of claim 32, wherein the power supply isconfigured to receive power from a standard wall outlet.